From 9bd23288cb811db42a5056b15e71cc3bb65d72b4 Mon Sep 17 00:00:00 2001 From: Jayke Date: Fri, 11 Oct 2024 15:27:38 +1000 Subject: [PATCH 01/18] Added functions to read and load nifti files --- recognition/dataset.py.py | 75 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 recognition/dataset.py.py diff --git a/recognition/dataset.py.py b/recognition/dataset.py.py new file mode 100644 index 000000000..573cfb978 --- /dev/null +++ b/recognition/dataset.py.py @@ -0,0 +1,75 @@ +import numpy as np +import nibabel as nib +from tqdm import tqdm +import glob + +def to_channels(arr:np.ndarray, dtype = np.uint8) -> np.ndarray: + channels = np.unique(arr) + res = np.zeros(arr.shape + (len(channels),), dtype = dtype) + for c in channels: + c = int(c) + res [... , c : c +1][ arr == c ] = 1 + + return res +# load medical image functions +def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np.float32, + getAffines = False, early_stop = False): + ''' + Load medical image data from names , cases list provided into a list for each . + + This function pre - allocates 4 D arrays for conv2d to avoid excessive memory usage . + + normImage : bool(normalise the image 0.0 - 1.0) + early_stop : Stop loading pre-maturely , leaves arrays mostly empty , for quick loading and testing scripts . + ''' + affines = [] + + # get fixed size + num = len (imageNames) + first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") + if len(first_case.shape) == 3: + first_case = first_case [: ,: ,0] # sometimes extra dims , remove + if categorical: + first_case = to_channels (first_case, dtype = dtype) + rows, cols, channels = first_case.shape + images = np.zeros((num, rows, cols, channels), dtype = dtype) + else: + rows, cols = first_case. shape + images = np.zeros((num, rows, cols), dtype = dtype) + + for i, inName in enumerate (tqdm(imageNames)): + niftiImage = nib.load(inName) + inImage = niftiImage.get_fdata(caching ="unchanged") # read disk only + affine = niftiImage.affine + if len (inImage.shape ) == 3: + inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data + inImage = inImage.astype(dtype) + if normImage: + # ~ inImage = inImage / np . linalg . norm ( inImage ) + # ~ inImage = 255. * inImage / inImage . max () + inImage = (inImage - inImage.mean()) / inImage.std() + if categorical: + inImage = to_channels(inImage, dtype = dtype) + images[i ,: ,: ,:] = inImage + else: + images[i ,: ,:] = inImage + + affines.append(affine) + if i > 20 and early_stop: + break + + if getAffines: + return images, affines + else: + return images + + +def load_train(path): + image_list = [] + for filename in glob.glob(path + '/*.nii.gz'): + image_list.append(filename) + print(image_list) + train_set = load_data_2D(image_list, normImage=True) + return train_set +print(glob.glob("C:\\Users\\jbrag\\Dev\\PatternAnalysis-2024\\recognition\\test" + '/*.nii.gz' )) +train_Y = load_train("C:\\Users\\jbrag\\Dev\\PatternAnalysis-2024\\recognition\\test") From 0ac7432ff1529840ebe90563f6a4a53d24034470 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sat, 12 Oct 2024 19:02:09 +1000 Subject: [PATCH 02/18] Added loader for train, test and validate for segmentation. Scales images to be same size. --- recognition/{dataset.py.py => dataset.py} | 31 +++++++++++++++++------ 1 file changed, 23 insertions(+), 8 deletions(-) rename recognition/{dataset.py.py => dataset.py} (71%) diff --git a/recognition/dataset.py.py b/recognition/dataset.py similarity index 71% rename from recognition/dataset.py.py rename to recognition/dataset.py index 573cfb978..de6a24a28 100644 --- a/recognition/dataset.py.py +++ b/recognition/dataset.py @@ -2,14 +2,17 @@ import nibabel as nib from tqdm import tqdm import glob +from matplotlib import pyplot +from matplotlib import image +import tensorflow as tf +import skimage.transform as sk def to_channels(arr:np.ndarray, dtype = np.uint8) -> np.ndarray: channels = np.unique(arr) res = np.zeros(arr.shape + (len(channels),), dtype = dtype) for c in channels: c = int(c) - res [... , c : c +1][ arr == c ] = 1 - + res [... , c : c +1][arr == c] = 1 return res # load medical image functions def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np.float32, @@ -27,10 +30,11 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np # get fixed size num = len (imageNames) first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") + first_case = sk.resize(first_case, (256,144), order=1, preserve_range=True) if len(first_case.shape) == 3: first_case = first_case [: ,: ,0] # sometimes extra dims , remove if categorical: - first_case = to_channels (first_case, dtype = dtype) + first_case = to_channels(first_case, dtype = dtype) rows, cols, channels = first_case.shape images = np.zeros((num, rows, cols, channels), dtype = dtype) else: @@ -40,6 +44,7 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np for i, inName in enumerate (tqdm(imageNames)): niftiImage = nib.load(inName) inImage = niftiImage.get_fdata(caching ="unchanged") # read disk only + inImage = sk.resize(inImage, (256,144), order=1, preserve_range=True) affine = niftiImage.affine if len (inImage.shape ) == 3: inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data @@ -49,7 +54,9 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np # ~ inImage = 255. * inImage / inImage . max () inImage = (inImage - inImage.mean()) / inImage.std() if categorical: + print(inImage.shape) inImage = to_channels(inImage, dtype = dtype) + print(inImage.shape) images[i ,: ,: ,:] = inImage else: images[i ,: ,:] = inImage @@ -64,12 +71,20 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np return images -def load_train(path): +def load(path, label=False): image_list = [] for filename in glob.glob(path + '/*.nii.gz'): image_list.append(filename) - print(image_list) - train_set = load_data_2D(image_list, normImage=True) + train_set = load_data_2D(image_list, normImage=True, categorical=label) return train_set -print(glob.glob("C:\\Users\\jbrag\\Dev\\PatternAnalysis-2024\\recognition\\test" + '/*.nii.gz' )) -train_Y = load_train("C:\\Users\\jbrag\\Dev\\PatternAnalysis-2024\\recognition\\test") + + +X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" +train_X = load(X_path + "train") +validate_X = load(X_path + "validate") +test_X = load(X_path + "test") + +seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" +train_Y = load(seg_path + "test", label=True) +validate_Y = load(seg_path + "validate", label=True) +test_Y = load(seg_path + "test", label=True) \ No newline at end of file From 3001366339f4f94decbcc0ce96734361215db776 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sat, 12 Oct 2024 19:42:47 +1000 Subject: [PATCH 03/18] Fixed one-hot encoding of labels and scaling of input images --- recognition/dataset.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recognition/dataset.py b/recognition/dataset.py index de6a24a28..d22fa542a 100644 --- a/recognition/dataset.py +++ b/recognition/dataset.py @@ -9,7 +9,7 @@ def to_channels(arr:np.ndarray, dtype = np.uint8) -> np.ndarray: channels = np.unique(arr) - res = np.zeros(arr.shape + (len(channels),), dtype = dtype) + res = np.zeros(arr.shape + (5,), dtype = dtype) for c in channels: c = int(c) res [... , c : c +1][arr == c] = 1 @@ -30,7 +30,8 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np # get fixed size num = len (imageNames) first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") - first_case = sk.resize(first_case, (256,144), order=1, preserve_range=True) + if not categorical: + first_case = sk.resize(first_case, (256,128), order=1, preserve_range=True) if len(first_case.shape) == 3: first_case = first_case [: ,: ,0] # sometimes extra dims , remove if categorical: @@ -44,7 +45,8 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np for i, inName in enumerate (tqdm(imageNames)): niftiImage = nib.load(inName) inImage = niftiImage.get_fdata(caching ="unchanged") # read disk only - inImage = sk.resize(inImage, (256,144), order=1, preserve_range=True) + if not categorical: + inImage = sk.resize(inImage, (256,128), order=1, preserve_range=True) affine = niftiImage.affine if len (inImage.shape ) == 3: inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data @@ -54,9 +56,7 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np # ~ inImage = 255. * inImage / inImage . max () inImage = (inImage - inImage.mean()) / inImage.std() if categorical: - print(inImage.shape) inImage = to_channels(inImage, dtype = dtype) - print(inImage.shape) images[i ,: ,: ,:] = inImage else: images[i ,: ,:] = inImage From 14b12a85e2ac26dedad395ce9f66d90de97d5511 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 13 Oct 2024 12:29:31 +1000 Subject: [PATCH 04/18] Fixed one_hot encoding of labels --- recognition/dataset.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/recognition/dataset.py b/recognition/dataset.py index d22fa542a..afc6a3c8d 100644 --- a/recognition/dataset.py +++ b/recognition/dataset.py @@ -9,7 +9,7 @@ def to_channels(arr:np.ndarray, dtype = np.uint8) -> np.ndarray: channels = np.unique(arr) - res = np.zeros(arr.shape + (5,), dtype = dtype) + res = np.zeros(arr.shape + (6,), dtype = dtype) for c in channels: c = int(c) res [... , c : c +1][arr == c] = 1 @@ -26,12 +26,11 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np early_stop : Stop loading pre-maturely , leaves arrays mostly empty , for quick loading and testing scripts . ''' affines = [] - + #count = 0 # get fixed size - num = len (imageNames) + num = len(imageNames) first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") - if not categorical: - first_case = sk.resize(first_case, (256,128), order=1, preserve_range=True) + first_case = sk.resize(first_case, (256,256)) if len(first_case.shape) == 3: first_case = first_case [: ,: ,0] # sometimes extra dims , remove if categorical: @@ -39,14 +38,13 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np rows, cols, channels = first_case.shape images = np.zeros((num, rows, cols, channels), dtype = dtype) else: - rows, cols = first_case. shape + rows, cols = first_case.shape images = np.zeros((num, rows, cols), dtype = dtype) for i, inName in enumerate (tqdm(imageNames)): niftiImage = nib.load(inName) inImage = niftiImage.get_fdata(caching ="unchanged") # read disk only - if not categorical: - inImage = sk.resize(inImage, (256,128), order=1, preserve_range=True) + inImage = sk.resize(inImage, (256,256)) affine = niftiImage.affine if len (inImage.shape ) == 3: inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data @@ -75,7 +73,7 @@ def load(path, label=False): image_list = [] for filename in glob.glob(path + '/*.nii.gz'): image_list.append(filename) - train_set = load_data_2D(image_list, normImage=True, categorical=label) + train_set = load_data_2D(image_list, normImage=False, categorical=label) return train_set @@ -85,6 +83,6 @@ def load(path, label=False): test_X = load(X_path + "test") seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" -train_Y = load(seg_path + "test", label=True) +train_Y = load(seg_path + "train", label=True) validate_Y = load(seg_path + "validate", label=True) test_Y = load(seg_path + "test", label=True) \ No newline at end of file From 99ca0f6e17b41c9d2e513a1df3befee99d60a9ae Mon Sep 17 00:00:00 2001 From: Jayke Date: Tue, 15 Oct 2024 17:02:05 +1000 Subject: [PATCH 05/18] Added modules.py with Unet model, moved files into folder --- recognition/{ => 2D_UNet_46991638}/README.md | 0 recognition/{ => 2D_UNet_46991638}/dataset.py | 0 recognition/2D_UNet_46991638/modules.py | 173 ++++++++++++++++++ 3 files changed, 173 insertions(+) rename recognition/{ => 2D_UNet_46991638}/README.md (100%) rename recognition/{ => 2D_UNet_46991638}/dataset.py (100%) create mode 100644 recognition/2D_UNet_46991638/modules.py diff --git a/recognition/README.md b/recognition/2D_UNet_46991638/README.md similarity index 100% rename from recognition/README.md rename to recognition/2D_UNet_46991638/README.md diff --git a/recognition/dataset.py b/recognition/2D_UNet_46991638/dataset.py similarity index 100% rename from recognition/dataset.py rename to recognition/2D_UNet_46991638/dataset.py diff --git a/recognition/2D_UNet_46991638/modules.py b/recognition/2D_UNet_46991638/modules.py new file mode 100644 index 000000000..981aafeaa --- /dev/null +++ b/recognition/2D_UNet_46991638/modules.py @@ -0,0 +1,173 @@ +import tensorflow as tf +#Modified from https://github.com/shakes76/PatternFlow/blob/master/recognition/MySolution/Methods.ipynb +def unet_model (): + filter_size=16 + input_layer = tf.keras.Input((256,256,1)) + + pre_conv = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same")(input_layer) + pre_conv = tf.keras.layers.LeakyReLU(alpha=.01)(pre_conv) + + +# context module 1 pre-activation residual block + conv1 = tf.keras.layers.BatchNormalization()(pre_conv) + conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1) + conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same" )(conv1) + conv1 = tf.keras.layers.Dropout(.3) (conv1) + conv1 = tf.keras.layers.BatchNormalization()(conv1) + conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1) + conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same")(conv1) + conv1 = tf.keras.layers.Add()([pre_conv,conv1]) + +# downsample and double number of feature maps + pool1 = tf.keras.layers.Conv2D(filter_size * 2, (3,3), (2,2) , padding='same')(conv1) + pool1 = tf.keras.layers.LeakyReLU(alpha=.01)(pool1) + +# context module 2 + conv2 = tf.keras.layers.BatchNormalization()(pool1) + conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2) + conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(conv2) + conv2 = tf.keras.layers.Dropout(.3) (conv2) + conv2 = tf.keras.layers.BatchNormalization()(conv2) + conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2) + conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(conv2) + conv2 = tf.keras.layers.Add()([pool1,conv2]) + +# downsample and double number of feature maps + pool2 = tf.keras.layers.Conv2D(filter_size*4, (3,3),(2,2), padding='same')(conv2) + pool2 = tf.keras.layers.LeakyReLU(alpha=.01)(pool2) + +# context module 3 + conv3 = tf.keras.layers.BatchNormalization()(pool2) + conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3) + conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(conv3) + conv3 = tf.keras.layers.Dropout(.3) (conv3) + conv3 = tf.keras.layers.BatchNormalization()(conv3) + conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3) + conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(conv3) + conv3 = tf.keras.layers.Add()([pool2,conv3]) + +# downsample and double number of feature maps + pool3 = tf.keras.layers.Conv2D(filter_size*8, (3,3),(2,2),padding='same')(conv3) + pool3 = tf.keras.layers.LeakyReLU(alpha=.01)(pool3) + +# context module 4 + conv4 = tf.keras.layers.BatchNormalization()(pool3) + conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4) + conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(conv4) + conv4 = tf.keras.layers.Dropout(.3) (conv4) + conv4 = tf.keras.layers.BatchNormalization()(conv4) + conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4) + conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(conv4) + conv4 = tf.keras.layers.Add()([pool3,conv4]) + + +# downsample and double number of feature maps + pool4 = tf.keras.layers.Conv2D(filter_size*16, (3,3),(2,2),padding='same')(conv4) + pool4 = tf.keras.layers.LeakyReLU(alpha=.01)(pool4) + +# context module 5 + # Middle + convm = tf.keras.layers.BatchNormalization()(pool4) + convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm) + convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding="same")(convm) + convm = tf.keras.layers.Dropout(.3) (convm) + convm = tf.keras.layers.BatchNormalization()(convm) + convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm) + convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding="same")(convm) + convm = tf.keras.layers.Add()([pool4,convm]) + + +#upsampling module 1 + deconv4 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(convm) + deconv4 = tf.keras.layers.Conv2D (filter_size *8, (3, 3) , padding="same")(deconv4) + deconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv4) + + +#concatatinate layers + uconv4 = tf.keras.layers.concatenate([deconv4, conv4], axis=3) + + +#localization module 1 + uconv4 = tf.keras.layers.Conv2D(filter_size * 16, (3, 3) , padding="same")(uconv4) + uconv4 = tf.keras.layers.BatchNormalization()(uconv4) + uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4) + uconv4 = tf.keras.layers.Conv2D(filter_size * 8, (1, 1), padding="same")(uconv4) + uconv4 = tf.keras.layers.BatchNormalization()(uconv4) + uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4) + +#upsampling module 2 + deconv3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv4) + deconv3 = tf.keras.layers.Conv2D (filter_size *4, (3, 3) , padding="same")(deconv3) + deconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv3) + + + +# concatatinate layers + uconv3 = tf.keras.layers.concatenate([deconv3, conv3], axis=3) + + +# localization module 2 + uconv3 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(uconv3) + uconv3 = tf.keras.layers.BatchNormalization()(uconv3) + uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3) + uconv3 = tf.keras.layers.Conv2D(filter_size * 4, (1, 1), padding="same")(uconv3) + uconv3 = tf.keras.layers.BatchNormalization()(uconv3) + uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3) + +# segmentation layer 1 + seg3 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv3) +# upscale segmented layer 1 + seg3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg3) + + +# Upsample module 3 + deconv2 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv3) + deconv2 = tf.keras.layers.Conv2D (filter_size *2, (3, 3) , padding="same")(deconv2) + deconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv2) + + +# concatination layer + uconv2 = tf.keras.layers.concatenate([deconv2, conv2], axis=3) + + +# localization module 3 + uconv2 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(uconv2) + uconv2 = tf.keras.layers.BatchNormalization()(uconv2) + uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2) + uconv2 = tf.keras.layers.Conv2D(filter_size * 2, (1, 1), padding="same")(uconv2) + uconv2 = tf.keras.layers.BatchNormalization()(uconv2) + uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2) + +# segmentation layer 2 + seg2 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv2) + +# add segmentation layer 1 and 2 + seg_32 = tf.keras.layers.Add()([seg3,seg2]) +# upscale sum segmentation layer 1 and 2 + seg_32 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg_32) + + +# Upsample module 4 + deconv1 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv2) + deconv1 = tf.keras.layers.Conv2D (filter_size *1, (3, 3) , padding="same")(deconv1) + deconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv1) + + +# concatination layer + uconv1 = tf.keras.layers.concatenate([deconv1, conv1], axis=3 ) + +#final convolution layer + uconv1 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(uconv1) + uconv1 = tf.keras.layers.BatchNormalization()(uconv1) + uconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv1) + +# final segmentation layer + seg1 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same' )(uconv1) + +# sum all segmentation layers + seg_sum = tf.keras.layers.Add()([seg1,seg_32]) + + + output_layer = tf.keras.layers.Conv2D(6, (3,3), padding='same' ,activation="softmax")(seg_sum) + model = tf.keras.Model( input_layer , outputs=output_layer) + return model From 4bf846c9e6dbcb281acb2506787a83d0d03eac86 Mon Sep 17 00:00:00 2001 From: Jayke Date: Tue, 22 Oct 2024 17:57:09 +1000 Subject: [PATCH 06/18] Added train.py - model learning loop --- recognition/2D_UNet_46991638/train.py | 67 +++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 recognition/2D_UNet_46991638/train.py diff --git a/recognition/2D_UNet_46991638/train.py b/recognition/2D_UNet_46991638/train.py new file mode 100644 index 000000000..69e17bf9d --- /dev/null +++ b/recognition/2D_UNet_46991638/train.py @@ -0,0 +1,67 @@ +import dataset as data +import modules +import tensorflow as tf +from matplotlib import pyplot +import numpy as np + +l_rate = 0.0005 +epochs = 50 + +X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" +train_X = data.load(X_path + "train") +validate_X = data.load(X_path + "validate") +test_X = data.load(X_path + "test") + +train_X = data.process_data(train_X) +validate_X = data.process_data(validate_X) +test_x = data.process_data(test_X) + + +t = test_X.copy() +t = t[...,np.newaxis] +test_X = t + +seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" +train_Y = data.load(seg_path + "train", label=True) + +validate_Y = data.load(seg_path + "validate", label=True) + +test_Y = data.load(seg_path + "test", label=True) +""" +print("X shapes") +print(train_X.shape) +print(validate_X.shape) +print(test_X.shape) +print("X is :") +print(np.amin(train_X)) +print(np.amax(train_X)) +print(train_X[1,1:5,1:5]) +print("Y is") +print(np.amin(train_Y)) +print(np.amax(train_Y)) +print(train_Y[1,1:5,1:5,:]) +print("Labeled shapes") +print(train_Y.shape) +print(validate_Y.shape) +print(test_Y.shape) +""" + +model = modules.unet_model() + +opt= tf.keras.optimizers.Adam(learning_rate = l_rate) +model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[tf.keras.losses.Dice]) +history = model.fit(train_X, train_Y, validation_data=(validate_X, validate_Y), batch_size=8,shuffle='True',epochs=epochs) + +print("Model evaluation:") +model.evaluate(test_X,test_Y) + +model.save_weights('UNet.weights.h5') + +#Plot loss function +pyplot.title('Dice Similarity Coefficient') +pyplot.plot([1 - x for x in history.history['dice']], color='blue', label='train') +pyplot.plot([1 - x for x in history.history['val_dice']], color='orange', label='test') +pyplot.legend(('training','validation')) +pyplot.savefig("Dice_Coeff") +pyplot.clf() + From 7d41827994b5a2e15270db32d09f0f08d5c8621b Mon Sep 17 00:00:00 2001 From: Jayke Date: Thu, 24 Oct 2024 04:16:22 +1000 Subject: [PATCH 07/18] Fixed number of filters in segmentation layers of model to reflect number of classes in dataset --- recognition/2D_UNet_46991638/modules.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recognition/2D_UNet_46991638/modules.py b/recognition/2D_UNet_46991638/modules.py index 981aafeaa..b4b57ec72 100644 --- a/recognition/2D_UNet_46991638/modules.py +++ b/recognition/2D_UNet_46991638/modules.py @@ -115,7 +115,7 @@ def unet_model (): uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3) # segmentation layer 1 - seg3 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv3) + seg3 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same')(uconv3) # upscale segmented layer 1 seg3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg3) @@ -139,7 +139,7 @@ def unet_model (): uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2) # segmentation layer 2 - seg2 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv2) + seg2 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same')(uconv2) # add segmentation layer 1 and 2 seg_32 = tf.keras.layers.Add()([seg3,seg2]) @@ -162,7 +162,7 @@ def unet_model (): uconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv1) # final segmentation layer - seg1 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same' )(uconv1) + seg1 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same' )(uconv1) # sum all segmentation layers seg_sum = tf.keras.layers.Add()([seg1,seg_32]) From 4a0ed4f95f84bfab72ddc95be8a79910b61a97d5 Mon Sep 17 00:00:00 2001 From: Jayke Date: Thu, 24 Oct 2024 04:21:05 +1000 Subject: [PATCH 08/18] Added data normalisation, fixed scaling of labels when resizing input using nearest neighboor interpolation --- recognition/2D_UNet_46991638/dataset.py | 39 +++++++++++++++---------- recognition/2D_UNet_46991638/train.py | 37 ++++++++++++++--------- 2 files changed, 48 insertions(+), 28 deletions(-) diff --git a/recognition/2D_UNet_46991638/dataset.py b/recognition/2D_UNet_46991638/dataset.py index afc6a3c8d..7a405750a 100644 --- a/recognition/2D_UNet_46991638/dataset.py +++ b/recognition/2D_UNet_46991638/dataset.py @@ -30,7 +30,12 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np # get fixed size num = len(imageNames) first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") - first_case = sk.resize(first_case, (256,256)) + if categorical: + #neareat-neigboor interpolation + first_case = sk.resize(first_case, (256,256), order=0, anti_aliasing=False, preserve_range=True) + else: + #bi-linear + first_case = sk.resize(first_case, (256, 256), order=1, anti_aliasing=True, preserve_range=True) if len(first_case.shape) == 3: first_case = first_case [: ,: ,0] # sometimes extra dims , remove if categorical: @@ -40,11 +45,17 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np else: rows, cols = first_case.shape images = np.zeros((num, rows, cols), dtype = dtype) - - for i, inName in enumerate (tqdm(imageNames)): + for i, inName in enumerate (tqdm(imageNames)): niftiImage = nib.load(inName) inImage = niftiImage.get_fdata(caching ="unchanged") # read disk only - inImage = sk.resize(inImage, (256,256)) + + if categorical: + #neareat-neigboor interpolation + inImage = sk.resize(inImage, (256,256), order=0, anti_aliasing=False, preserve_range=True) + else: + #bi-linear + inImage = sk.resize(inImage, (256, 256), order=1, anti_aliasing=True, preserve_range=True) + affine = niftiImage.affine if len (inImage.shape ) == 3: inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data @@ -56,13 +67,13 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np if categorical: inImage = to_channels(inImage, dtype = dtype) images[i ,: ,: ,:] = inImage - else: + else: images[i ,: ,:] = inImage affines.append(affine) if i > 20 and early_stop: break - + print("H") if getAffines: return images, affines else: @@ -76,13 +87,11 @@ def load(path, label=False): train_set = load_data_2D(image_list, normImage=False, categorical=label) return train_set +def process_data(data_set): + # the method normalizes training images and adds 4th dimention -X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" -train_X = load(X_path + "train") -validate_X = load(X_path + "validate") -test_X = load(X_path + "test") - -seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" -train_Y = load(seg_path + "train", label=True) -validate_Y = load(seg_path + "validate", label=True) -test_Y = load(seg_path + "test", label=True) \ No newline at end of file + train_set = data_set + train_set = (train_set - np.mean(train_set))/ np.std(train_set) + train_set= (train_set- np.amin(train_set))/ np.amax(train_set- np.amin(train_set)) + train_set = train_set[...,np.newaxis] + return train_set \ No newline at end of file diff --git a/recognition/2D_UNet_46991638/train.py b/recognition/2D_UNet_46991638/train.py index 69e17bf9d..bd81de083 100644 --- a/recognition/2D_UNet_46991638/train.py +++ b/recognition/2D_UNet_46991638/train.py @@ -4,8 +4,8 @@ from matplotlib import pyplot import numpy as np -l_rate = 0.0005 -epochs = 50 +l_rate = 0.005 +epochs = 30 X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" train_X = data.load(X_path + "train") @@ -14,12 +14,7 @@ train_X = data.process_data(train_X) validate_X = data.process_data(validate_X) -test_x = data.process_data(test_X) - - -t = test_X.copy() -t = t[...,np.newaxis] -test_X = t +test_X = data.process_data(test_X) seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" train_Y = data.load(seg_path + "train", label=True) @@ -27,7 +22,6 @@ validate_Y = data.load(seg_path + "validate", label=True) test_Y = data.load(seg_path + "test", label=True) -""" print("X shapes") print(train_X.shape) print(validate_X.shape) @@ -35,22 +29,40 @@ print("X is :") print(np.amin(train_X)) print(np.amax(train_X)) +print(np.amin(test_X)) +print(np.amax(test_X)) print(train_X[1,1:5,1:5]) print("Y is") print(np.amin(train_Y)) print(np.amax(train_Y)) -print(train_Y[1,1:5,1:5,:]) +print(np.amin(test_Y)) +print(np.amax(test_Y)) print("Labeled shapes") print(train_Y.shape) print(validate_Y.shape) print(test_Y.shape) -""" +class DataGenerator(tf.keras.utils.Sequence): + def __init__(self, x_set, y_set, batch_size): + self.x, self.y = x_set, y_set + self.batch_size = batch_size + + def __len__(self): + return int(np.floor(len(self.x) / self.batch_size)) + + def __getitem__(self, idx): + batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] + batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] + return batch_x, batch_y + model = modules.unet_model() +train_gen = DataGenerator(train_X, train_Y, batch_size=32) +val_gen = DataGenerator(validate_X, validate_Y, batch_size=32) opt= tf.keras.optimizers.Adam(learning_rate = l_rate) model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[tf.keras.losses.Dice]) -history = model.fit(train_X, train_Y, validation_data=(validate_X, validate_Y), batch_size=8,shuffle='True',epochs=epochs) +history = model.fit(train_gen, validation_data=val_gen, epochs=epochs) +#history = model.fit(train_X, train_Y, validation_data=(validate_X, validate_Y), batch_size=20,shuffle='True',epochs=epochs) print("Model evaluation:") model.evaluate(test_X,test_Y) @@ -64,4 +76,3 @@ pyplot.legend(('training','validation')) pyplot.savefig("Dice_Coeff") pyplot.clf() - From 6b9bcf7a55e2193097804e7a931305a8f24bf297 Mon Sep 17 00:00:00 2001 From: Jayke Date: Thu, 24 Oct 2024 23:26:53 +1000 Subject: [PATCH 09/18] Moved DataGenerator to dataset.py --- recognition/2D_UNet_46991638/dataset.py | 15 ++++++- recognition/2D_UNet_46991638/modules.py | 58 ++++++++++++------------- recognition/2D_UNet_46991638/train.py | 16 +------ 3 files changed, 45 insertions(+), 44 deletions(-) diff --git a/recognition/2D_UNet_46991638/dataset.py b/recognition/2D_UNet_46991638/dataset.py index 7a405750a..d56a7a8a5 100644 --- a/recognition/2D_UNet_46991638/dataset.py +++ b/recognition/2D_UNet_46991638/dataset.py @@ -94,4 +94,17 @@ def process_data(data_set): train_set = (train_set - np.mean(train_set))/ np.std(train_set) train_set= (train_set- np.amin(train_set))/ np.amax(train_set- np.amin(train_set)) train_set = train_set[...,np.newaxis] - return train_set \ No newline at end of file + return train_set + +class DataGenerator(tf.keras.utils.Sequence): + def __init__(self, x_set, y_set, batch_size): + self.x, self.y = x_set, y_set + self.batch_size = batch_size + + def __len__(self): + return int(np.floor(len(self.x) / self.batch_size)) + + def __getitem__(self, idx): + batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] + batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] + return batch_x, batch_y \ No newline at end of file diff --git a/recognition/2D_UNet_46991638/modules.py b/recognition/2D_UNet_46991638/modules.py index b4b57ec72..13ccb29fc 100644 --- a/recognition/2D_UNet_46991638/modules.py +++ b/recognition/2D_UNet_46991638/modules.py @@ -5,74 +5,74 @@ def unet_model (): input_layer = tf.keras.Input((256,256,1)) pre_conv = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same")(input_layer) - pre_conv = tf.keras.layers.LeakyReLU(alpha=.01)(pre_conv) + pre_conv = tf.keras.layers.LeakyReLU(negative_slope=.01)(pre_conv) # context module 1 pre-activation residual block conv1 = tf.keras.layers.BatchNormalization()(pre_conv) - conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1) + conv1 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv1) conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same" )(conv1) conv1 = tf.keras.layers.Dropout(.3) (conv1) conv1 = tf.keras.layers.BatchNormalization()(conv1) - conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1) + conv1 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv1) conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding="same")(conv1) conv1 = tf.keras.layers.Add()([pre_conv,conv1]) # downsample and double number of feature maps pool1 = tf.keras.layers.Conv2D(filter_size * 2, (3,3), (2,2) , padding='same')(conv1) - pool1 = tf.keras.layers.LeakyReLU(alpha=.01)(pool1) + pool1 = tf.keras.layers.LeakyReLU(negative_slope=.01)(pool1) # context module 2 conv2 = tf.keras.layers.BatchNormalization()(pool1) - conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2) + conv2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv2) conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(conv2) conv2 = tf.keras.layers.Dropout(.3) (conv2) conv2 = tf.keras.layers.BatchNormalization()(conv2) - conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2) + conv2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv2) conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(conv2) conv2 = tf.keras.layers.Add()([pool1,conv2]) # downsample and double number of feature maps pool2 = tf.keras.layers.Conv2D(filter_size*4, (3,3),(2,2), padding='same')(conv2) - pool2 = tf.keras.layers.LeakyReLU(alpha=.01)(pool2) + pool2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(pool2) # context module 3 conv3 = tf.keras.layers.BatchNormalization()(pool2) - conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3) + conv3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv3) conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(conv3) conv3 = tf.keras.layers.Dropout(.3) (conv3) conv3 = tf.keras.layers.BatchNormalization()(conv3) - conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3) + conv3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv3) conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(conv3) conv3 = tf.keras.layers.Add()([pool2,conv3]) # downsample and double number of feature maps pool3 = tf.keras.layers.Conv2D(filter_size*8, (3,3),(2,2),padding='same')(conv3) - pool3 = tf.keras.layers.LeakyReLU(alpha=.01)(pool3) + pool3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(pool3) # context module 4 conv4 = tf.keras.layers.BatchNormalization()(pool3) - conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4) + conv4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv4) conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(conv4) conv4 = tf.keras.layers.Dropout(.3) (conv4) conv4 = tf.keras.layers.BatchNormalization()(conv4) - conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4) + conv4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(conv4) conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(conv4) conv4 = tf.keras.layers.Add()([pool3,conv4]) # downsample and double number of feature maps pool4 = tf.keras.layers.Conv2D(filter_size*16, (3,3),(2,2),padding='same')(conv4) - pool4 = tf.keras.layers.LeakyReLU(alpha=.01)(pool4) + pool4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(pool4) # context module 5 # Middle convm = tf.keras.layers.BatchNormalization()(pool4) - convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm) + convm = tf.keras.layers.LeakyReLU(negative_slope=.01)(convm) convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding="same")(convm) convm = tf.keras.layers.Dropout(.3) (convm) convm = tf.keras.layers.BatchNormalization()(convm) - convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm) + convm = tf.keras.layers.LeakyReLU(negative_slope=.01)(convm) convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding="same")(convm) convm = tf.keras.layers.Add()([pool4,convm]) @@ -80,7 +80,7 @@ def unet_model (): #upsampling module 1 deconv4 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(convm) deconv4 = tf.keras.layers.Conv2D (filter_size *8, (3, 3) , padding="same")(deconv4) - deconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv4) + deconv4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(deconv4) #concatatinate layers @@ -90,15 +90,15 @@ def unet_model (): #localization module 1 uconv4 = tf.keras.layers.Conv2D(filter_size * 16, (3, 3) , padding="same")(uconv4) uconv4 = tf.keras.layers.BatchNormalization()(uconv4) - uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4) + uconv4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv4) uconv4 = tf.keras.layers.Conv2D(filter_size * 8, (1, 1), padding="same")(uconv4) uconv4 = tf.keras.layers.BatchNormalization()(uconv4) - uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4) + uconv4 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv4) #upsampling module 2 deconv3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv4) deconv3 = tf.keras.layers.Conv2D (filter_size *4, (3, 3) , padding="same")(deconv3) - deconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv3) + deconv3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(deconv3) @@ -109,13 +109,13 @@ def unet_model (): # localization module 2 uconv3 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding="same")(uconv3) uconv3 = tf.keras.layers.BatchNormalization()(uconv3) - uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3) + uconv3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv3) uconv3 = tf.keras.layers.Conv2D(filter_size * 4, (1, 1), padding="same")(uconv3) uconv3 = tf.keras.layers.BatchNormalization()(uconv3) - uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3) + uconv3 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv3) # segmentation layer 1 - seg3 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same')(uconv3) + seg3 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv3) # upscale segmented layer 1 seg3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg3) @@ -123,7 +123,7 @@ def unet_model (): # Upsample module 3 deconv2 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv3) deconv2 = tf.keras.layers.Conv2D (filter_size *2, (3, 3) , padding="same")(deconv2) - deconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv2) + deconv2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(deconv2) # concatination layer @@ -133,13 +133,13 @@ def unet_model (): # localization module 3 uconv2 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding="same")(uconv2) uconv2 = tf.keras.layers.BatchNormalization()(uconv2) - uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2) + uconv2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv2) uconv2 = tf.keras.layers.Conv2D(filter_size * 2, (1, 1), padding="same")(uconv2) uconv2 = tf.keras.layers.BatchNormalization()(uconv2) - uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2) + uconv2 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv2) # segmentation layer 2 - seg2 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same')(uconv2) + seg2 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same')(uconv2) # add segmentation layer 1 and 2 seg_32 = tf.keras.layers.Add()([seg3,seg2]) @@ -150,7 +150,7 @@ def unet_model (): # Upsample module 4 deconv1 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv2) deconv1 = tf.keras.layers.Conv2D (filter_size *1, (3, 3) , padding="same")(deconv1) - deconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv1) + deconv1 = tf.keras.layers.LeakyReLU(negative_slope=.01)(deconv1) # concatination layer @@ -159,10 +159,10 @@ def unet_model (): #final convolution layer uconv1 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding="same")(uconv1) uconv1 = tf.keras.layers.BatchNormalization()(uconv1) - uconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv1) + uconv1 = tf.keras.layers.LeakyReLU(negative_slope=.01)(uconv1) # final segmentation layer - seg1 = tf.keras.layers.Conv2D(4, (3,3), activation="softmax", padding='same' )(uconv1) + seg1 = tf.keras.layers.Conv2D(6, (3,3), activation="softmax", padding='same' )(uconv1) # sum all segmentation layers seg_sum = tf.keras.layers.Add()([seg1,seg_32]) diff --git a/recognition/2D_UNet_46991638/train.py b/recognition/2D_UNet_46991638/train.py index bd81de083..1cddb7b3a 100644 --- a/recognition/2D_UNet_46991638/train.py +++ b/recognition/2D_UNet_46991638/train.py @@ -42,22 +42,10 @@ print(validate_Y.shape) print(test_Y.shape) -class DataGenerator(tf.keras.utils.Sequence): - def __init__(self, x_set, y_set, batch_size): - self.x, self.y = x_set, y_set - self.batch_size = batch_size - - def __len__(self): - return int(np.floor(len(self.x) / self.batch_size)) - - def __getitem__(self, idx): - batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] - batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] - return batch_x, batch_y model = modules.unet_model() -train_gen = DataGenerator(train_X, train_Y, batch_size=32) -val_gen = DataGenerator(validate_X, validate_Y, batch_size=32) +train_gen = data.DataGenerator(train_X, train_Y, batch_size=32) +val_gen = data.DataGenerator(validate_X, validate_Y, batch_size=32) opt= tf.keras.optimizers.Adam(learning_rate = l_rate) model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[tf.keras.losses.Dice]) From d692c943d64e311ee7d8c2a2799498f583ec0404 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sat, 26 Oct 2024 21:51:01 +1000 Subject: [PATCH 10/18] Added predict.py for model evaluation. Moved some data loading from train to dataset --- recognition/2D_UNet_46991638/dataset.py | 28 +++++++++++++++++-- recognition/2D_UNet_46991638/predict.py | 36 +++++++++++++++++++++++++ recognition/2D_UNet_46991638/train.py | 21 ++++++++++----- 3 files changed, 76 insertions(+), 9 deletions(-) create mode 100644 recognition/2D_UNet_46991638/predict.py diff --git a/recognition/2D_UNet_46991638/dataset.py b/recognition/2D_UNet_46991638/dataset.py index d56a7a8a5..e2ba62061 100644 --- a/recognition/2D_UNet_46991638/dataset.py +++ b/recognition/2D_UNet_46991638/dataset.py @@ -73,7 +73,6 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np affines.append(affine) if i > 20 and early_stop: break - print("H") if getAffines: return images, affines else: @@ -107,4 +106,29 @@ def __len__(self): def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] - return batch_x, batch_y \ No newline at end of file + return batch_x, batch_y + +def get_X_data(path, only_test = False): + path += "keras_slices_" + if not only_test: + train_X = load(path + "train") + validate_X = load(path + "validate") + test_X = load(path + "test") + if not only_test: + train_X = process_data(train_X) + validate_X = process_data(validate_X) + test_X = process_data(test_X) + if not only_test: + return train_X, validate_X, test_X + return test_X + +def get_Y_data(path, only_test = False): + path += "keras_slices_seg_" + if not only_test: + train_Y = load(path + "train", label=True) + validate_Y = load(path + "validate", label=True) + test_Y = load(path + "test", label=True) + + if only_test: + return test_Y + return train_Y, validate_Y, test_Y \ No newline at end of file diff --git a/recognition/2D_UNet_46991638/predict.py b/recognition/2D_UNet_46991638/predict.py new file mode 100644 index 000000000..9902d8b81 --- /dev/null +++ b/recognition/2D_UNet_46991638/predict.py @@ -0,0 +1,36 @@ +import modules +import numpy as np +import dataset as data +import tensorflow as tf +from matplotlib import pyplot +from matplotlib import image + +#Load only the test data +path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/" +test_X = data.get_X_data(path, only_test=True) +test_Y = data.get_Y_data(path, only_test=True) + +#Load model +model = modules.unet_model() +opt= tf.keras.optimizers.Adam(learning_rate=.0005) +model.compile (optimizer=opt, loss= 'CategoricalCrossentropy', metrics=['accuracy']) +model.load_weights('UNet.weights.h5') +print(test_X.shape) +print(test_Y.shape) +#Evaluate Model against test data +print("Model evaluation:") +model.evaluate (test_X,test_Y) + +#Generate predictions +predictions = np.round(model.predict(test_X)) + +#Plot predicted segment and true segments +im = 69 +for i in range (6): + pyplot.title("Prediction_{}".format(i)) + pyplot.imshow(predictions[im,:,:,i]) + pyplot.savefig("graphs/Prediction_{}.png".format(i)) + pyplot.title("True_{}".format(i)) + pyplot.imshow(test_Y[im,:,:,i]) + pyplot.savefig("graphs/True_{}.png".format(i)) + diff --git a/recognition/2D_UNet_46991638/train.py b/recognition/2D_UNet_46991638/train.py index 1cddb7b3a..870ef76e4 100644 --- a/recognition/2D_UNet_46991638/train.py +++ b/recognition/2D_UNet_46991638/train.py @@ -5,8 +5,10 @@ import numpy as np l_rate = 0.005 -epochs = 30 +epochs = 40 + +""" X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" train_X = data.load(X_path + "train") validate_X = data.load(X_path + "validate") @@ -22,6 +24,11 @@ validate_Y = data.load(seg_path + "validate", label=True) test_Y = data.load(seg_path + "test", label=True) +""" + +path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/" +train_X, validate_X, test_X = data.get_X_data(path) +train_Y, validate_Y, test_Y = data.get_Y_data(path) print("X shapes") print(train_X.shape) print(validate_X.shape) @@ -29,14 +36,14 @@ print("X is :") print(np.amin(train_X)) print(np.amax(train_X)) -print(np.amin(test_X)) -print(np.amax(test_X)) +print(np.amin(validate_X)) +print(np.amax(validate_X)) print(train_X[1,1:5,1:5]) print("Y is") -print(np.amin(train_Y)) -print(np.amax(train_Y)) -print(np.amin(test_Y)) -print(np.amax(test_Y)) +print(np.amin(validate_Y)) +print(np.amax(validate_Y)) +print(np.amin(validate_Y)) +print(np.amax(validate_Y)) print("Labeled shapes") print(train_Y.shape) print(validate_Y.shape) From 45b4f28627175857ce49c54b2f83b95e065151e7 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 22:52:56 +1000 Subject: [PATCH 11/18] Added comments and removed commented out code and unnecessary prints --- recognition/2D_UNet_46991638/dataset.py | 11 +++---- recognition/2D_UNet_46991638/predict.py | 7 ++-- recognition/2D_UNet_46991638/train.py | 44 ++----------------------- 3 files changed, 12 insertions(+), 50 deletions(-) diff --git a/recognition/2D_UNet_46991638/dataset.py b/recognition/2D_UNet_46991638/dataset.py index e2ba62061..eea8dd85c 100644 --- a/recognition/2D_UNet_46991638/dataset.py +++ b/recognition/2D_UNet_46991638/dataset.py @@ -26,10 +26,10 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np early_stop : Stop loading pre-maturely , leaves arrays mostly empty , for quick loading and testing scripts . ''' affines = [] - #count = 0 # get fixed size num = len(imageNames) first_case = nib.load(imageNames[0]).get_fdata(caching="unchanged") + #rescale image if categorical: #neareat-neigboor interpolation first_case = sk.resize(first_case, (256,256), order=0, anti_aliasing=False, preserve_range=True) @@ -61,8 +61,6 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np inImage = inImage[: ,: ,0] # sometimes extra dims in HipMRI_study data inImage = inImage.astype(dtype) if normImage: - # ~ inImage = inImage / np . linalg . norm ( inImage ) - # ~ inImage = 255. * inImage / inImage . max () inImage = (inImage - inImage.mean()) / inImage.std() if categorical: inImage = to_channels(inImage, dtype = dtype) @@ -78,7 +76,7 @@ def load_data_2D (imageNames, normImage = False, categorical = False, dtype = np else: return images - +#Load all the nifti images in the given apth def load(path, label=False): image_list = [] for filename in glob.glob(path + '/*.nii.gz'): @@ -86,15 +84,16 @@ def load(path, label=False): train_set = load_data_2D(image_list, normImage=False, categorical=label) return train_set -def process_data(data_set): +#Normalise and scale to [0, 1] +def process_data(train_set): # the method normalizes training images and adds 4th dimention - train_set = data_set train_set = (train_set - np.mean(train_set))/ np.std(train_set) train_set= (train_set- np.amin(train_set))/ np.amax(train_set- np.amin(train_set)) train_set = train_set[...,np.newaxis] return train_set +#Data Generator class made with assitance of ChatGPT class DataGenerator(tf.keras.utils.Sequence): def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set diff --git a/recognition/2D_UNet_46991638/predict.py b/recognition/2D_UNet_46991638/predict.py index 9902d8b81..f8e637115 100644 --- a/recognition/2D_UNet_46991638/predict.py +++ b/recognition/2D_UNet_46991638/predict.py @@ -12,11 +12,12 @@ #Load model model = modules.unet_model() -opt= tf.keras.optimizers.Adam(learning_rate=.0005) -model.compile (optimizer=opt, loss= 'CategoricalCrossentropy', metrics=['accuracy']) +opt= tf.keras.optimizers.Adam(learning_rate = 0.005) +model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[tf.keras.losses.Dice]) model.load_weights('UNet.weights.h5') print(test_X.shape) print(test_Y.shape) + #Evaluate Model against test data print("Model evaluation:") model.evaluate (test_X,test_Y) @@ -25,7 +26,7 @@ predictions = np.round(model.predict(test_X)) #Plot predicted segment and true segments -im = 69 +im = 69 #which image to plot for i in range (6): pyplot.title("Prediction_{}".format(i)) pyplot.imshow(predictions[im,:,:,i]) diff --git a/recognition/2D_UNet_46991638/train.py b/recognition/2D_UNet_46991638/train.py index 870ef76e4..130cd9a89 100644 --- a/recognition/2D_UNet_46991638/train.py +++ b/recognition/2D_UNet_46991638/train.py @@ -4,52 +4,15 @@ from matplotlib import pyplot import numpy as np -l_rate = 0.005 -epochs = 40 +l_rate = 0.0001 +epochs = 50 -""" -X_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_" -train_X = data.load(X_path + "train") -validate_X = data.load(X_path + "validate") -test_X = data.load(X_path + "test") - -train_X = data.process_data(train_X) -validate_X = data.process_data(validate_X) -test_X = data.process_data(test_X) - -seg_path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/keras_slices_seg_" -train_Y = data.load(seg_path + "train", label=True) - -validate_Y = data.load(seg_path + "validate", label=True) - -test_Y = data.load(seg_path + "test", label=True) -""" - path = "/home/groups/comp3710/HipMRI_Study_open/keras_slices_data/" train_X, validate_X, test_X = data.get_X_data(path) train_Y, validate_Y, test_Y = data.get_Y_data(path) -print("X shapes") -print(train_X.shape) -print(validate_X.shape) -print(test_X.shape) -print("X is :") -print(np.amin(train_X)) -print(np.amax(train_X)) -print(np.amin(validate_X)) -print(np.amax(validate_X)) -print(train_X[1,1:5,1:5]) -print("Y is") -print(np.amin(validate_Y)) -print(np.amax(validate_Y)) -print(np.amin(validate_Y)) -print(np.amax(validate_Y)) -print("Labeled shapes") -print(train_Y.shape) -print(validate_Y.shape) -print(test_Y.shape) - +#Initialise model and data generators model = modules.unet_model() train_gen = data.DataGenerator(train_X, train_Y, batch_size=32) val_gen = data.DataGenerator(validate_X, validate_Y, batch_size=32) @@ -57,7 +20,6 @@ opt= tf.keras.optimizers.Adam(learning_rate = l_rate) model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[tf.keras.losses.Dice]) history = model.fit(train_gen, validation_data=val_gen, epochs=epochs) -#history = model.fit(train_X, train_Y, validation_data=(validate_X, validate_Y), batch_size=20,shuffle='True',epochs=epochs) print("Model evaluation:") model.evaluate(test_X,test_Y) From 3e9b8513c54f52ac21795ec44100e338bc0edcdd Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 22:54:46 +1000 Subject: [PATCH 12/18] Added README --- recognition/2D_UNet_46991638/README.md | 52 +++++++++++++++++++++----- 1 file changed, 42 insertions(+), 10 deletions(-) diff --git a/recognition/2D_UNet_46991638/README.md b/recognition/2D_UNet_46991638/README.md index 32c99e899..28a07dd2b 100644 --- a/recognition/2D_UNet_46991638/README.md +++ b/recognition/2D_UNet_46991638/README.md @@ -1,10 +1,42 @@ -# Recognition Tasks -Various recognition tasks solved in deep learning frameworks. - -Tasks may include: -* Image Segmentation -* Object detection -* Graph node classification -* Image super resolution -* Disease classification -* Generative modelling with StyleGAN and Stable Diffusion \ No newline at end of file +# Segmenting Hip-MRI to identify Prostate Cancer using 2D-UNet +Every year tens of thousands of men are diagnosed with prostate cancer, mostly effecting elderly men[1]. The identification of abnormal or enlarged prostates in a HipMRI could therefore be useful for assisting doctors in early detection. + +## 2D UNet +![alt text](image.png)[2] +The UNet is a model mostly used for segmenting images and is especially effective on small datasets, which are common in medical imaging[3]. The model consists of an encoding network which extracts features from the input followed by a decoding network which creates the segmentation mask[2]. A key difference between the UNet earlier image segmentation models is that the UNet utilises skip connections which allow for finer details in the original image to be carried through the model into the segmentation mask. + +## Implementation +This model is design to be used on 2D slices of HipMRI data which can be found [here](https://data.csiro.au/collection/csiro:51392v2). The dataset was preprocesses into 2D-Slices. Some images were of different sizes so each image was normalised to a size of 256x256 and the pixel values normalised 0-1. The preprocessing already separated the data into train, validate and test sets which were of size 11640, 660 and 540 respectively. Using this distribution aims to maximise the available data for training while still providing enough data for testing and validation. The model itself was adapted from [this](https://github.com/shakes76/PatternFlow/tree/master/recognition/MySolution) source, modifying the output layers and filter size for this problem. + +## Results + +After some tuning, a preliminary test run of 50 epochs shows only a slight increase in accuracy for the validation data while the training data improves significantly. This disparity could be a result of overfitting, although due to the variance in the validation data it also seemed likely that the learning rate was too high. +![alt text](graphs\Dice_Coeff1.png) + +In hopes to improve this the learning rate was lowered from $10^{-3}$ to $10^{-4}$ giving the following result. +![alt text](graphs\Dice_Coeff.png) +As expected the training data is learned slower, however there is not a clear difference in results for the validation data. In both instances the validation dice score roughly oscillates between 0.7 and 0.725. + +The higher learning rate gave a slightly better evaluation on the test set with a dice score of 0.7507 compared to 0.7462. Which is not significant enough to suggest the learning rate is a meaningful factor in the models lack of accuracy. Regardless the first (higher) learning rate is used for model evaluation. Other hyperparameters were tested with other values such as filter size, batch size and number of epochs. However they gave similarly ineffective results with the validation data stagnating quickly. + +Running inference on this iteration of the model gives these segmentation masks for each class +| Predicted Mask | True Mask | +| ------------- | ------------- | +| ![alt text](graphs\Prediction_0.png)| ![alt text](graphs\True_0.png) | +| ![alt text](graphs\Prediction_1.png)| ![alt text](graphs\True_1.png) | +| ![alt text](graphs\Prediction_2.png)| ![alt text](graphs\True_2.png) | +| ![alt text](graphs\Prediction_3.png)| ![alt text](graphs\True_3.png) | +| ![alt text](graphs\Prediction_4.png)| ![alt text](graphs\True_4.png) | +| ![alt text](graphs\Prediction_5.png)| ![alt text](graphs\True_5.png) | +Clearly this result leaves much to be desired and while the model may not be very useful in its current state it provides a starting point for further study. + + +## Dependencies and Reproducibility +The model is implemented using tensorflow 2.17.0 utilising the nibabel library to read nifti images. The parameters in the provided model are the same that were used to retrieve the displayed results. Which should allow the results to be reproduced or built upon if desired. + +## References +[1] https://www.cancer.org.au/cancer-information/types-of-cancer/prostate-cancer + +[2] https://medium.com/analytics-vidhya/what-is-unet-157314c87634 + +[3] https://github.com/NITR098/Awesome-U-Net \ No newline at end of file From 04d51813d7c77b134536f77f9ffb12408fbce793 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 22:55:17 +1000 Subject: [PATCH 13/18] Added folder with graphs of prediction and loss over iterations --- .../2D_UNet_46991638/graphs/Dice_Coeff.png | Bin 0 -> 32499 bytes .../2D_UNet_46991638/graphs/Dice_Coeff1.png | Bin 0 -> 32291 bytes .../2D_UNet_46991638/graphs/Prediction_0.png | Bin 0 -> 15237 bytes .../2D_UNet_46991638/graphs/Prediction_1.png | Bin 0 -> 21381 bytes .../2D_UNet_46991638/graphs/Prediction_2.png | Bin 0 -> 16193 bytes .../2D_UNet_46991638/graphs/Prediction_3.png | Bin 0 -> 11700 bytes .../2D_UNet_46991638/graphs/Prediction_4.png | Bin 0 -> 10848 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z0wwT}+0Unkj`>4B?H2o+Dj_O*zU Date: Sun, 27 Oct 2024 23:00:52 +1000 Subject: [PATCH 14/18] Fixed images in README --- recognition/2D_UNet_46991638/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/recognition/2D_UNet_46991638/README.md b/recognition/2D_UNet_46991638/README.md index 28a07dd2b..e0605ef08 100644 --- a/recognition/2D_UNet_46991638/README.md +++ b/recognition/2D_UNet_46991638/README.md @@ -11,10 +11,10 @@ This model is design to be used on 2D slices of HipMRI data which can be found [ ## Results After some tuning, a preliminary test run of 50 epochs shows only a slight increase in accuracy for the validation data while the training data improves significantly. This disparity could be a result of overfitting, although due to the variance in the validation data it also seemed likely that the learning rate was too high. -![alt text](graphs\Dice_Coeff1.png) +![alt text](graphs/Dice_Coeff1.png) In hopes to improve this the learning rate was lowered from $10^{-3}$ to $10^{-4}$ giving the following result. -![alt text](graphs\Dice_Coeff.png) +![alt text](graphs/Dice_Coeff.png) As expected the training data is learned slower, however there is not a clear difference in results for the validation data. In both instances the validation dice score roughly oscillates between 0.7 and 0.725. The higher learning rate gave a slightly better evaluation on the test set with a dice score of 0.7507 compared to 0.7462. Which is not significant enough to suggest the learning rate is a meaningful factor in the models lack of accuracy. Regardless the first (higher) learning rate is used for model evaluation. Other hyperparameters were tested with other values such as filter size, batch size and number of epochs. However they gave similarly ineffective results with the validation data stagnating quickly. @@ -22,12 +22,12 @@ The higher learning rate gave a slightly better evaluation on the test set with Running inference on this iteration of the model gives these segmentation masks for each class | Predicted Mask | True Mask | | ------------- | ------------- | -| ![alt text](graphs\Prediction_0.png)| ![alt text](graphs\True_0.png) | -| ![alt text](graphs\Prediction_1.png)| ![alt text](graphs\True_1.png) | -| ![alt text](graphs\Prediction_2.png)| ![alt text](graphs\True_2.png) | -| ![alt text](graphs\Prediction_3.png)| ![alt text](graphs\True_3.png) | -| ![alt text](graphs\Prediction_4.png)| ![alt text](graphs\True_4.png) | -| ![alt text](graphs\Prediction_5.png)| ![alt text](graphs\True_5.png) | +| ![alt text](graphs/Prediction_0.png)| ![alt text](graphs/True_0.png) | +| ![alt text](graphs/Prediction_1.png)| ![alt text](graphs/True_1.png) | +| ![alt text](graphs/Prediction_2.png)| ![alt text](graphs/True_2.png) | +| ![alt text](graphs/Prediction_3.png)| ![alt text](graphs/True_3.png) | +| ![alt text](graphs/Prediction_4.png)| ![alt text](graphs/True_4.png) | +| ![alt text](graphs/Prediction_5.png)| ![alt text](graphs/True_5.png) | Clearly this result leaves much to be desired and while the model may not be very useful in its current state it provides a starting point for further study. From fc8408d6425d8194c23484e0978aad5818af1f7a Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 23:13:58 +1000 Subject: [PATCH 15/18] Improved placement of images in README --- recognition/2D_UNet_46991638/README.md | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/recognition/2D_UNet_46991638/README.md b/recognition/2D_UNet_46991638/README.md index e0605ef08..dabf62857 100644 --- a/recognition/2D_UNet_46991638/README.md +++ b/recognition/2D_UNet_46991638/README.md @@ -11,23 +11,27 @@ This model is design to be used on 2D slices of HipMRI data which can be found [ ## Results After some tuning, a preliminary test run of 50 epochs shows only a slight increase in accuracy for the validation data while the training data improves significantly. This disparity could be a result of overfitting, although due to the variance in the validation data it also seemed likely that the learning rate was too high. + ![alt text](graphs/Dice_Coeff1.png) In hopes to improve this the learning rate was lowered from $10^{-3}$ to $10^{-4}$ giving the following result. + ![alt text](graphs/Dice_Coeff.png) + As expected the training data is learned slower, however there is not a clear difference in results for the validation data. In both instances the validation dice score roughly oscillates between 0.7 and 0.725. The higher learning rate gave a slightly better evaluation on the test set with a dice score of 0.7507 compared to 0.7462. Which is not significant enough to suggest the learning rate is a meaningful factor in the models lack of accuracy. Regardless the first (higher) learning rate is used for model evaluation. Other hyperparameters were tested with other values such as filter size, batch size and number of epochs. However they gave similarly ineffective results with the validation data stagnating quickly. Running inference on this iteration of the model gives these segmentation masks for each class -| Predicted Mask | True Mask | -| ------------- | ------------- | -| ![alt text](graphs/Prediction_0.png)| ![alt text](graphs/True_0.png) | -| ![alt text](graphs/Prediction_1.png)| ![alt text](graphs/True_1.png) | -| ![alt text](graphs/Prediction_2.png)| ![alt text](graphs/True_2.png) | -| ![alt text](graphs/Prediction_3.png)| ![alt text](graphs/True_3.png) | -| ![alt text](graphs/Prediction_4.png)| ![alt text](graphs/True_4.png) | -| ![alt text](graphs/Prediction_5.png)| ![alt text](graphs/True_5.png) | + + + + + + + + + Clearly this result leaves much to be desired and while the model may not be very useful in its current state it provides a starting point for further study. From c5b3ac7fd9f0fbd5a7dfa06fe6bed19295a33773 Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 23:25:39 +1000 Subject: [PATCH 16/18] Fixed title image in README --- recognition/2D_UNet_46991638/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recognition/2D_UNet_46991638/README.md b/recognition/2D_UNet_46991638/README.md index dabf62857..dec3fc184 100644 --- a/recognition/2D_UNet_46991638/README.md +++ b/recognition/2D_UNet_46991638/README.md @@ -2,7 +2,8 @@ Every year tens of thousands of men are diagnosed with prostate cancer, mostly effecting elderly men[1]. The identification of abnormal or enlarged prostates in a HipMRI could therefore be useful for assisting doctors in early detection. ## 2D UNet -![alt text](image.png)[2] +![alt text](graphs/UNet.png)[2] + The UNet is a model mostly used for segmenting images and is especially effective on small datasets, which are common in medical imaging[3]. The model consists of an encoding network which extracts features from the input followed by a decoding network which creates the segmentation mask[2]. A key difference between the UNet earlier image segmentation models is that the UNet utilises skip connections which allow for finer details in the original image to be carried through the model into the segmentation mask. ## Implementation From 038895e892d47c064a055eb48c615c2cb973b2ec Mon Sep 17 00:00:00 2001 From: Jayke Date: Sun, 27 Oct 2024 23:26:47 +1000 Subject: [PATCH 17/18] Added UNet image to graphs folder for README --- recognition/2D_UNet_46991638/graphs/UNet.png | Bin 0 -> 247841 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/2D_UNet_46991638/graphs/UNet.png diff --git a/recognition/2D_UNet_46991638/graphs/UNet.png b/recognition/2D_UNet_46991638/graphs/UNet.png new file mode 100644 index 0000000000000000000000000000000000000000..7e489dd51a9115f537576e89fcdb70b544a508fc GIT binary patch literal 247841 zcmeFXg;QMn6Fr#V4#C~sox$DR-66OS!686!*TJ12!7T)Lg1Zx(0E4^3lDzl6zuo;4 zwzg`fX6{VYt-k%~K7IOhN2n-CBO~A=ynFW!Syo0u?cF=b*mv(B^59_Jewn4F`1tnm 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