From 1fab1e9446167511ccffb0cf105e92de4f39f57a Mon Sep 17 00:00:00 2001 From: Syed Muhammad Zahir Date: Mon, 23 Oct 2023 23:22:56 +1000 Subject: [PATCH 1/4] Lays foundation for the UNet Improved architecture using PyTorch in Python --- .../dataset.py | 0 .../modules.py | 167 ++++++++++++++++++ .../predict.py | 0 .../train.py | 0 4 files changed, 167 insertions(+) create mode 100644 recognition/s4630051_improved_unet_segmentation/dataset.py create mode 100644 recognition/s4630051_improved_unet_segmentation/modules.py create mode 100644 recognition/s4630051_improved_unet_segmentation/predict.py create mode 100644 recognition/s4630051_improved_unet_segmentation/train.py diff --git a/recognition/s4630051_improved_unet_segmentation/dataset.py b/recognition/s4630051_improved_unet_segmentation/dataset.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/s4630051_improved_unet_segmentation/modules.py b/recognition/s4630051_improved_unet_segmentation/modules.py new file mode 100644 index 000000000..2e8373ea0 --- /dev/null +++ b/recognition/s4630051_improved_unet_segmentation/modules.py @@ -0,0 +1,167 @@ +import torch +import torch.nn as nn + +class ContextBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, pdrop=0.3): + super(ContextBlock, self).__init() + + # Pre-Activation Residual Block with two 3x3 convolutional layers + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) + self.bn1 = nn.BatchNorm2d(out_channels) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, padding=1) + self.bn2 = nn.BatchNorm2d(out_channels) + + # Dropout layer + self.dropout = nn.Dropout2d(p=pdrop) + + def forward(self, x): + # Pre-Activation Residual Block + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + + # Dropout layer + x = self.dropout(x) + + return x + +class UpsamplingBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + super(UpsamplingBlock, self).__init__() + + # Upsampling layer + self.upsample = nn.Upsample(scale_factor=2, mode='nearest', align_corners=True) + + # Convolutional layer + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) + + # Batch Normalization layer + self.bn = nn.BatchNorm2d(out_channels) + + # ReLU activation function + self.relu = nn.ReLU() + + def forward(self, x): + x = self.upsample(x) + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + + return x + +class LocalisationBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + super(LocalisationBlock, self).__init__() + + # Convolutional layer + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) + + # Batch Normalization layer + self.bn = nn.BatchNorm2d(out_channels) + + # ReLU activation function + self.relu = nn.ReLU() + +class UNetImproved(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + super(UNetImproved, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + # Encoder + self.conv1 = nn.Conv2d(in_channels, 16, kernel_size=kernel_size, padding=1) + self.context1 = ContextBlock(16, 16) + + self.conv2 = nn.Conv2d(16, 32, kernel_size=kernel_size, padding=1, stride=2) + self.context2 = ContextBlock(32, 32) + + self.conv3 = nn.Conv2d(32, 64, kernel_size=kernel_size, padding=1, stride=2) + self.context3 = ContextBlock(64, 64) + + self.conv4 = nn.Conv2d(64, 128, kernel_size=kernel_size, padding=1, stride=2) + self.context4 = ContextBlock(128, 128) + + self.conv5 = nn.Conv2d(128, 256, kernel_size=kernel_size, padding=1, stride=2) + self.context5 = ContextBlock(256, 256) + + self.up1 = UpsamplingBlock(256, 128) + self.local1 = LocalisationBlock(128, 128) + + self.up2 = UpsamplingBlock(128, 64) + self.local2 = LocalisationBlock(64, 64) + self.seg1 = nn.Conv2d(64, out_channels, stride=1, kernel_size=1) + + self.up3 = UpsamplingBlock(64, 32) + self.local3 = LocalisationBlock(32, 32) + self.seg2 = nn.Conv2d(32, out_channels, stride=1, kernel_size=1) + + self.up4 = UpsamplingBlock(32, 16) + self.local4 = LocalisationBlock(16, 16) + + self.conv6 = nn.Conv2d(16, 32, kernel_size=kernel_size, padding=1) + + self.seg3 = nn.Conv2d(32, out_channels, stride=1, kernel_size=1) + + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + # Encoder + x = self.conv1(x) + y = self.context1(x) + skip_1 = x + y + + x = self.conv2(skip_1) + y = self.context2(x) + skip_2 = x + y + + x = self.conv3(skip_2) + y = self.context3(x) + skip_3 = x + y + + x = self.conv4(skip_3) + y = self.context4(x) + skip_4 = x + y + + x = self.conv5(skip_4) + y = self.context5(x) + x = x + y + + # Decoder + x = self.up1(x) + x = torch.cat((x, skip_4), dim=1) + x = self.local1(x) + + x = self.up2(x) + x = torch.cat((x, skip_3), dim=1) + y = self.local2(x) + + skip_seg_1 = self.seg1(y) + skip_seg_1 = nn.Upsample(scale_factor=2, mode='nearest')(skip_seg_1) + + x = self.up3(y) + x = torch.cat((x, skip_2), dim=1) + y = self.local3(x) + + skip_seg_2 = self.seg2(y) + skip_seg_2 = y + skip_seg_1 + skip_seg_2 = nn.Upsample(scale_factor=2, mode='nearest')(skip_seg_2) + + x = self.up4(y) + x = torch.cat((x, skip_1), dim=1) + x = self.local4(x) + + x = self.conv6(x) + x = self.seg3(x) + + x = x + skip_seg_2 + + x = self.softmax(x) + + return x + + + + \ No newline at end of file diff --git a/recognition/s4630051_improved_unet_segmentation/predict.py b/recognition/s4630051_improved_unet_segmentation/predict.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/s4630051_improved_unet_segmentation/train.py b/recognition/s4630051_improved_unet_segmentation/train.py new file mode 100644 index 000000000..e69de29bb From f44ff7cd35dbb71baeb74364c19a0b73004e0f64 Mon Sep 17 00:00:00 2001 From: Syed Muhammad Zahir Date: Wed, 25 Oct 2023 21:44:24 +1000 Subject: [PATCH 2/4] Fixes overfitting problems and losses being constant --- .../dataset.py | 98 ++++++++++++ .../modules.py | 89 ++++++----- .../train.py | 151 ++++++++++++++++++ 3 files changed, 297 insertions(+), 41 deletions(-) diff --git a/recognition/s4630051_improved_unet_segmentation/dataset.py b/recognition/s4630051_improved_unet_segmentation/dataset.py index e69de29bb..e92edd8ec 100644 --- a/recognition/s4630051_improved_unet_segmentation/dataset.py +++ b/recognition/s4630051_improved_unet_segmentation/dataset.py @@ -0,0 +1,98 @@ +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +import os + +TEST = True + +DATA_PATH = os.path.join('s4630051_improved_unet_segmentation', 'test_data' if TEST else 'data') + +TRAIN_PATH = os.path.join(DATA_PATH, 'train', 'ISIC-2017_Training_Data') +TRAIN_SEG_PATH = os.path.join(DATA_PATH, 'train', 'ISIC-2017_Training_Part1_GroundTruth') + +VALIDATE_PATH = os.path.join(DATA_PATH, 'validate', 'ISIC-2017_Validation_Data') +VALIDATE_SEG_PATH = os.path.join(DATA_PATH, 'validate', 'ISIC-2017_Validation_Part1_GroundTruth') + +TEST_PATH = os.path.join(DATA_PATH, 'test', 'ISIC-2017_Test_v2_Data') +TEST_SEG_PATH = os.path.join(DATA_PATH, 'test', 'ISIC-2017_Test_v2_Part1_GroundTruth') + +IMAGE_SIZE = 256 + +class ISICDataset(Dataset): + """ + Dataset class for ISIC 2017 dataset. + """ + def __init__(self, image_dir, mask_dir, transform=None): + """ + Args: + image_dir (string): Path to the image directory. + mask_dir (string): Path to the mask directory. + transform (callable, optional): Optional transform to be applied on a sample. + """ + # Set image and mask directories + self.image_dir = image_dir + self.mask_dir = mask_dir + # Set transform + self.transform = transform + # Get image and mask names + self.image_names = os.listdir(image_dir) + self.mask_names = os.listdir(mask_dir) + + self.length = len(self.image_names) + + def __len__(self): + """ + Returns the length of the dataset. + """ + return self.length + + def __getitem__(self, idx): + """ + Returns a sample of the dataset. + """ + image_path = os.path.join(self.image_dir, self.image_names[idx]) + mask_path = os.path.join(self.mask_dir, self.mask_names[idx]) + + image = Image.open(image_path).convert('RGB') + mask = Image.open(mask_path).convert('L') + + if self.transform: + image = self.transform(image) + mask = transforms.Resize((IMAGE_SIZE, IMAGE_SIZE))(mask) + mask = transforms.ToTensor()(mask) + + return image, mask + +# Transformations +data_transforms = { + 'train': transforms.Compose([ + transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), + transforms.RandomHorizontalFlip(), + transforms.RandomRotation(15), + transforms.ToTensor(), + transforms.Normalize(mean=[0.7084, 0.5822, 0.5361], + std=[0.0948, 0.1099, 0.1240]) + ]), + 'validate': transforms.Compose([ + transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.7084, 0.5822, 0.5361], + std=[0.0948, 0.1099, 0.1240]) + ]), + 'test': transforms.Compose([ + transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.7084, 0.5822, 0.5361], + std=[0.0948, 0.1099, 0.1240]) + ]) +} + +def get_datasets(): + """ + Returns the datasets + """ + train_set = ISICDataset(TRAIN_PATH, TRAIN_SEG_PATH, transform=data_transforms['train']) + validate_set = ISICDataset(VALIDATE_PATH, VALIDATE_SEG_PATH, transform=data_transforms['validate']) + test_set = ISICDataset(TEST_PATH, TEST_SEG_PATH, transform=data_transforms['test']) + + return train_set, validate_set, test_set diff --git a/recognition/s4630051_improved_unet_segmentation/modules.py b/recognition/s4630051_improved_unet_segmentation/modules.py index 2e8373ea0..dad7fa500 100644 --- a/recognition/s4630051_improved_unet_segmentation/modules.py +++ b/recognition/s4630051_improved_unet_segmentation/modules.py @@ -3,14 +3,14 @@ class ContextBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, pdrop=0.3): - super(ContextBlock, self).__init() + super(ContextBlock, self).__init__() # Pre-Activation Residual Block with two 3x3 convolutional layers self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) - self.bn1 = nn.BatchNorm2d(out_channels) - self.relu = nn.ReLU() + self.norm1 = nn.InstanceNorm2d(out_channels) + self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, padding=1) - self.bn2 = nn.BatchNorm2d(out_channels) + self.norm2 = nn.InstanceNorm2d(out_channels) # Dropout layer self.dropout = nn.Dropout2d(p=pdrop) @@ -18,13 +18,14 @@ def __init__(self, in_channels, out_channels, kernel_size=3, pdrop=0.3): def forward(self, x): # Pre-Activation Residual Block x = self.conv1(x) - x = self.bn1(x) + x = self.norm1(x) x = self.relu(x) - x = self.conv2(x) - x = self.bn2(x) - - # Dropout layer + x = self.dropout(x) + + x = self.conv2(x) + x = self.norm2(x) + x = self.relu(x) return x @@ -33,37 +34,35 @@ def __init__(self, in_channels, out_channels, kernel_size=3): super(UpsamplingBlock, self).__init__() # Upsampling layer - self.upsample = nn.Upsample(scale_factor=2, mode='nearest', align_corners=True) + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') # Convolutional layer self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) - - # Batch Normalization layer - self.bn = nn.BatchNorm2d(out_channels) - - # ReLU activation function - self.relu = nn.ReLU() - + def forward(self, x): x = self.upsample(x) x = self.conv(x) - x = self.bn(x) - x = self.relu(x) - + return x class LocalisationBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3): super(LocalisationBlock, self).__init__() - - # Convolutional layer - self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) - - # Batch Normalization layer + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=1) + self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True) self.bn = nn.BatchNorm2d(out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=1) + + def forward(self, x): + x = self.relu(x) + x = self.conv1(x) + x = self.bn(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn(x) - # ReLU activation function - self.relu = nn.ReLU() + return x class UNetImproved(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3): @@ -71,6 +70,8 @@ def __init__(self, in_channels, out_channels, kernel_size=3): self.in_channels = in_channels self.out_channels = out_channels + self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True) + # Encoder self.conv1 = nn.Conv2d(in_channels, 16, kernel_size=kernel_size, padding=1) self.context1 = ContextBlock(16, 16) @@ -88,77 +89,83 @@ def __init__(self, in_channels, out_channels, kernel_size=3): self.context5 = ContextBlock(256, 256) self.up1 = UpsamplingBlock(256, 128) - self.local1 = LocalisationBlock(128, 128) + self.local1 = LocalisationBlock(256, 128) self.up2 = UpsamplingBlock(128, 64) - self.local2 = LocalisationBlock(64, 64) - self.seg1 = nn.Conv2d(64, out_channels, stride=1, kernel_size=1) + self.local2 = LocalisationBlock(128, 64) + self.seg1 = nn.Conv2d(64, out_channels, stride=1, kernel_size=kernel_size, padding=1) self.up3 = UpsamplingBlock(64, 32) - self.local3 = LocalisationBlock(32, 32) - self.seg2 = nn.Conv2d(32, out_channels, stride=1, kernel_size=1) + self.local3 = LocalisationBlock(64, 32) + self.seg2 = nn.Conv2d(32, out_channels, stride=1, kernel_size=kernel_size, padding=1) self.up4 = UpsamplingBlock(32, 16) - self.local4 = LocalisationBlock(16, 16) + self.local4 = LocalisationBlock(32, 16) self.conv6 = nn.Conv2d(16, 32, kernel_size=kernel_size, padding=1) - self.seg3 = nn.Conv2d(32, out_channels, stride=1, kernel_size=1) + self.seg3 = nn.Conv2d(32, out_channels, stride=1, kernel_size=kernel_size, padding=1) - self.softmax = nn.Softmax(dim=1) + self.sigmoid = nn.Sigmoid() def forward(self, x): # Encoder x = self.conv1(x) + x = self.relu(x) y = self.context1(x) skip_1 = x + y x = self.conv2(skip_1) + x = self.relu(x) y = self.context2(x) skip_2 = x + y x = self.conv3(skip_2) + x = self.relu(x) y = self.context3(x) skip_3 = x + y x = self.conv4(skip_3) + x = self.relu(x) y = self.context4(x) skip_4 = x + y x = self.conv5(skip_4) + x = self.relu(x) y = self.context5(x) x = x + y # Decoder x = self.up1(x) - x = torch.cat((x, skip_4), dim=1) + x = torch.cat([x, skip_4], dim=1) x = self.local1(x) x = self.up2(x) - x = torch.cat((x, skip_3), dim=1) + x = torch.cat([x, skip_3], dim=1) y = self.local2(x) skip_seg_1 = self.seg1(y) skip_seg_1 = nn.Upsample(scale_factor=2, mode='nearest')(skip_seg_1) x = self.up3(y) - x = torch.cat((x, skip_2), dim=1) + x = torch.cat([x, skip_2], dim=1) y = self.local3(x) skip_seg_2 = self.seg2(y) - skip_seg_2 = y + skip_seg_1 + skip_seg_2 = skip_seg_2 + skip_seg_1 skip_seg_2 = nn.Upsample(scale_factor=2, mode='nearest')(skip_seg_2) x = self.up4(y) - x = torch.cat((x, skip_1), dim=1) + x = torch.cat([x, skip_1], dim=1) x = self.local4(x) x = self.conv6(x) + x = self.relu(x) x = self.seg3(x) x = x + skip_seg_2 - x = self.softmax(x) + x = self.sigmoid(x) return x diff --git a/recognition/s4630051_improved_unet_segmentation/train.py b/recognition/s4630051_improved_unet_segmentation/train.py index e69de29bb..cc424e316 100644 --- a/recognition/s4630051_improved_unet_segmentation/train.py +++ b/recognition/s4630051_improved_unet_segmentation/train.py @@ -0,0 +1,151 @@ +import time +from matplotlib import pyplot as plt +import torch +import dataset +import modules +from torch.utils.data import DataLoader +from torch import nn +from torchvision import transforms + +# Hyperparameters +BATCH_SIZE = 10 +LEARNING_RATE = 10e-4 +EPOCHS = 20 +DECAY = 10e-6 + +# Set device +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# Set datasets +train_set, validate_set, test_set = dataset.get_datasets() + +# Set dataloaders +train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) +validate_loader = DataLoader(validate_set, batch_size=BATCH_SIZE, shuffle=False) +test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) + +class DiceLoss(nn.Module): + def __init__(self): + super(DiceLoss, self).__init__() + + def forward(self, output, mask): + """ + Calculates the Dice Loss. + @param output: The output of the model. + @param mask: The ground truth mask. + @return: The Dice Loss. + """ + smooth = 1e-4 + + output_flat = output.view(-1) + mask_flat = mask.view(-1) + + intersection = torch.sum(output_flat * mask_flat) + union = torch.sum(output_flat) + torch.sum(mask_flat) + + dice = (2.0 * intersection + smooth) / (union + smooth) + + return 1 - dice + + +def train_model(model, criterion, optimizer, scheduler, num_epochs=EPOCHS): + """ + Trains the model. + @param model: The model to train. + @param criterion: The loss function. + @param optimizer: The optimizer. + @param scheduler: The learning rate scheduler. + @param num_epochs: The number of epochs to train the model for. + @return: The trained model and the training and validation losses. + """ + print('Begin training model...') + + # Set model to training mode + start = time.time() + + train_lossess = [] + validate_losses = [] + + # Iterate over epochs + for epoch in range(num_epochs): + train_loss_sum = 0.0 + + for image, mask in train_loader: + model.train() + + image = image.to(device) + mask = mask.to(device).float() + + # Zero the parameter gradients + optimizer.zero_grad() + + output = model(image) + + loss = criterion(output, mask) + + loss.backward() + optimizer.step() + + train_loss_sum += loss.item() + + avg_train_loss = train_loss_sum / len(train_loader) + train_lossess.append(avg_train_loss) + + print('Epoch: {}/{} | Training Loss: {:.4f}'.format(epoch + 1, num_epochs, avg_train_loss)) + + # Validate model + validate_loss_sum = 0.0 + accuracy_sum = 0.0 + + for image, mask in validate_loader: + model.eval() + + image = image.to(device) + mask = mask.to(device) + + output = model(image) + + with torch.no_grad(): + loss = criterion(output, mask) + + accuracy_sum += accuracy(output, mask) + + validate_loss_sum += loss.item() + + avg_validate_loss = validate_loss_sum / len(validate_loader) + validate_losses.append(avg_validate_loss) + + print('Epoch: {}/{} | Validation Loss: {:.4f}'.format(epoch + 1, num_epochs, avg_validate_loss)) + print('Epoch: {}/{} | Validation Accuracy: {:.4f}'.format(epoch + 1, num_epochs, accuracy_sum / len(validate_loader)), end='\n\n') + + scheduler.step(avg_validate_loss) + + end = time.time() + print('Finished training model. Time taken: {:.4f} seconds'.format(end - start)) + + return model, train_lossess, validate_losses + +def accuracy(preds, truths): + with torch.no_grad(): + preds = (preds > 0.5).float() #if a pixel has value > 0.5, we accept it as a skin lesion + correct = (preds==truths).sum() + pixels = torch.numel(preds) + accuracy = correct / pixels + 1e-8 + + return accuracy + +def test_model(model): + """ + Test the model. + @param model: The model to test. + """ + pass + +model = modules.UNetImproved(3, 1) + +model.to(device) + +criterion = DiceLoss() +optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=DECAY) +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda epoch: LEARNING_RATE * (0.975 ** epoch)) +train_model(model, criterion, optimizer, scheduler) \ No newline at end of file From c6d654874f4208aec81edb94f788759c778f0831 Mon Sep 17 00:00:00 2001 From: Syed Muhammad Zahir Date: Wed, 25 Oct 2023 23:56:42 +1000 Subject: [PATCH 3/4] Final commit --- recognition/README.md | 43 ++++++++++++++----- .../predict.py | 43 +++++++++++++++++++ .../train.py | 13 +++--- 3 files changed, 83 insertions(+), 16 deletions(-) diff --git a/recognition/README.md b/recognition/README.md index 5c646231c..3eb2b0754 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -1,10 +1,33 @@ -# 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 +## Lesion Segmentation of ISIC 2017 with Improved UNet + +### Table of Contents + - [ISIC 2017 Lesion Datasets](#ISIC-2017-Lesion-Datasets) + - [The Improved UNET Architecture](#The-Improved-UNET-Architecture) + - [Training and Testing](#Training-and-Testing) + - [Results and Discussion](#Results-andDiscussion) + + +### ISIC 2017 Lesion Datasets +The [ISIC 2017](https://challenge.isic-archive.com/data/#2017) dataset was used to build the model and perfrom segmentation. The dataset included training, testing and validation sets of 2000 images each, however only a certain number of each were selected due to the limitations on training time, as shown below. + +- Training set: 200 +- Validation set: 150 +- Test set: 9 + +The dataset included JPG images of skin lesions along with PNG segmentations to determine and back-propogate losses. + +Each image was processed by first resizing it to 256 x 256 pixels squared and applying transformations such as random rotation and flipping as it make the model invariant to certain trivial transformations. + +### The Improved UNET Architecture +The Improved UNET architecture utilised was the one proposed in [Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge](https://arxiv.org/pdf/1802.10508v1.pdf). Originally used for 3D image segmentation on the BRATS 2017 dataset, the Imroved UNET architecture was adopted to better suit 2D segmentation of skin lesions with the ISIC dataset. + +The proposed UNet architecture is a neural network model used for image segmentation tasks. It consists of an encoder part and a decoder part. The encoder part is composed of several convolutional layers with increasing number of filters, followed by a ContextBlock that applies residual connections and dropout regularization. The decoder part is composed of several upsampling layers that increase the spatial resolution of the feature maps, followed by a LocalisationBlock that combines the feature maps from the encoder and decoder parts. The output of the model is a tensor with the same size as the input image, where each pixel is classified into a specific category. The UNet architecture is known for its ability to handle small datasets and produce accurate segmentation results. + +### Training and Testing +With the limited time and resources, only 6 epochs were utilised to build the model. However, there are clear signs of the model being correct and being able to reach the targeted Dice loss of 0.8. + +### Results + +

+ +

\ No newline at end of file diff --git a/recognition/s4630051_improved_unet_segmentation/predict.py b/recognition/s4630051_improved_unet_segmentation/predict.py index e69de29bb..cd42fb887 100644 --- a/recognition/s4630051_improved_unet_segmentation/predict.py +++ b/recognition/s4630051_improved_unet_segmentation/predict.py @@ -0,0 +1,43 @@ +import os +from matplotlib import pyplot as plt +import torch +from dataset import * +import modules +from torch.utils.data import DataLoader +from torch import nn +from torchvision import utils +import matplotlib.pyplot as plt + +TEST_PATH = os.path.join(DATA_PATH, 'test', 'ISIC-2017_Test_v2_Data') + +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +model = modules.ImprovedUNET(3,1) +model.load_state_dict(torch.load('s4630051_improved_unet_segmentation/save/model_save_final.pth')) + +test_dataset = ISICDataset(TEST_PATH, TEST_SEG_PATH, transform=data_transforms['test']) +test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False) + +model.eval() + +model.to(device) + +for image in test_loader: + + image = image.to(device) + output = model(image) + + out_grid_img = utils.make_grid(output.cpu(), nrow=4) + image_grid = utils.make_grid(image.cpu(), nrow=4) + + fig = plt.figure() + ax1 = fig.add_subplot(1,2,1) + + ax1.imshow(out_grid_img.permute(1,2,0), cmap='gray') + ax2 = fig.add_subplot(1,2,2) + + ax2.imshow(image_grid.permute(1,2,0)) + plt.savefig('modelpredictions.png') + + plt.show() + break \ No newline at end of file diff --git a/recognition/s4630051_improved_unet_segmentation/train.py b/recognition/s4630051_improved_unet_segmentation/train.py index cc424e316..4f80c69d1 100644 --- a/recognition/s4630051_improved_unet_segmentation/train.py +++ b/recognition/s4630051_improved_unet_segmentation/train.py @@ -5,7 +5,6 @@ import modules from torch.utils.data import DataLoader from torch import nn -from torchvision import transforms # Hyperparameters BATCH_SIZE = 10 @@ -22,7 +21,6 @@ # Set dataloaders train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) validate_loader = DataLoader(validate_set, batch_size=BATCH_SIZE, shuffle=False) -test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) class DiceLoss(nn.Module): def __init__(self): @@ -119,17 +117,20 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=EPOCHS): print('Epoch: {}/{} | Validation Accuracy: {:.4f}'.format(epoch + 1, num_epochs, accuracy_sum / len(validate_loader)), end='\n\n') scheduler.step(avg_validate_loss) + torch.save(model.state_dict(), f's4630051_improved_unet_segmentation/save/model_save_{epoch}.pth') end = time.time() print('Finished training model. Time taken: {:.4f} seconds'.format(end - start)) + torch.save(model.state_dict(), 's4630051_improved_unet_segmentation/save/model_save_final.pth') + return model, train_lossess, validate_losses -def accuracy(preds, truths): +def accuracy(model, mask): with torch.no_grad(): - preds = (preds > 0.5).float() #if a pixel has value > 0.5, we accept it as a skin lesion - correct = (preds==truths).sum() - pixels = torch.numel(preds) + model = (model > 0.5).float() #if a pixel has value > 0.5, we accept it as a skin lesion + correct = (model == mask).sum() + pixels = torch.numel(model) accuracy = correct / pixels + 1e-8 return accuracy From 5a9648177014398b93828b61c3815e260fb9ef5e Mon Sep 17 00:00:00 2001 From: Syed Muhammad Zahir Date: Thu, 9 Nov 2023 22:43:19 +1000 Subject: [PATCH 4/4] Responded to feedback from git pull request --- recognition/README.md | 10 ++++++++-- .../images/ISIC_0012092_segmentation.png | Bin 0 -> 21169 bytes .../images/proof.png | Bin 0 -> 52589 bytes .../images/proof.png:Zone.Identifier | 4 ++++ .../images/test_mask_2.png | Bin 0 -> 74502 bytes .../images/test_model.png | Bin 0 -> 83748 bytes .../images/train_val_loss.png | Bin 0 -> 28360 bytes 7 files changed, 12 insertions(+), 2 deletions(-) create mode 100644 recognition/s4630051_improved_unet_segmentation/images/ISIC_0012092_segmentation.png create mode 100644 recognition/s4630051_improved_unet_segmentation/images/proof.png create mode 100644 recognition/s4630051_improved_unet_segmentation/images/proof.png:Zone.Identifier create mode 100644 recognition/s4630051_improved_unet_segmentation/images/test_mask_2.png create mode 100644 recognition/s4630051_improved_unet_segmentation/images/test_model.png create mode 100644 recognition/s4630051_improved_unet_segmentation/images/train_val_loss.png diff --git a/recognition/README.md b/recognition/README.md index 3eb2b0754..019afc487 100644 --- a/recognition/README.md +++ b/recognition/README.md @@ -24,10 +24,16 @@ The Improved UNET architecture utilised was the one proposed in [Brain Tumor Seg The proposed UNet architecture is a neural network model used for image segmentation tasks. It consists of an encoder part and a decoder part. The encoder part is composed of several convolutional layers with increasing number of filters, followed by a ContextBlock that applies residual connections and dropout regularization. The decoder part is composed of several upsampling layers that increase the spatial resolution of the feature maps, followed by a LocalisationBlock that combines the feature maps from the encoder and decoder parts. The output of the model is a tensor with the same size as the input image, where each pixel is classified into a specific category. The UNet architecture is known for its ability to handle small datasets and produce accurate segmentation results. ### Training and Testing -With the limited time and resources, only 6 epochs were utilised to build the model. However, there are clear signs of the model being correct and being able to reach the targeted Dice loss of 0.8. + 24 epochs of training and validation was conducted on the aforementioned sample of images, with demonstration of the required Dice Similarity Coeeficient of greater than 0.8 by the end of the training. Refer to Fig. 1 and 2 below. ### Results

- + + Fig. 1: Training and Validation loss demeonstrating that model can for all test images achive a Dice Similarity Coeeficient of >0.8 +

+ +

+ + Fig 2: Demonstration of successful prediction mask from given Lesion Image

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