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Dataset can be downloaded from Kaggle (see references).
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The source code is in 'jupyter notebook' directory.
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The 'SIIM-ACR Dataset Reading' jupyter notebook can be used to first read the dataset files and make the data split into train, validation and test sets.
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We used Google Colab for model training. Upload the dataset files to google drive and set the corresponding paths in the notebook.
We implmented multiple models inlcuding U-Net, Attention U-Net, Tiramisu: FCDenseNet-103 and Tiramisu: FCDenseNet-103 with Attention Gates.
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Kaggle: https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/overview
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Dataset: https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/data
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Attention U-Net: Learning Where to Look for the Pancreas, CoRR, 2018 klemek için tıklayın
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The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation, (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)