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The project explores different architectural approaches including U-Net and DeepLabV3+ with ResNet50 for semantic segmentation tasks in histopathological images.\n\n## Dataset\n\nThe PanNuke dataset consists of:\n- 189,744 labeled nuclei with instance segmentation masks\n- 7,901 images (256×256 pixels)\n- 19 tissue types\n- 5 cell categories (Neoplastic, Inflammatory, Connective, Dead, Epithelial)\n- Images captured at x40 magnification (0.25 µm/pixel resolution)\n\n## Implementation\n\nThe project includes three main implementations:\n1. Basic U-Net architecture\n2. ResNet50 with U-Net\n3. ResNet50 with DeepLabV3+\n\nEach implementation is available in separate Jupyter notebooks in the `notebooks/` directory.\n\n## Methods\n\nThe implementations utilize state-of-the-art deep learning architectures for semantic segmentation:\n- U-Net: Convolutional network architecture specifically designed for biomedical image segmentation\n- ResNet50: Deep residual network used as a backbone for feature extraction\n- DeepLabV3+: Advanced semantic segmentation architecture incorporating atrous convolutions\n\n## Reference Papers\n\n- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)\n\n## License\n\nThis project is licensed under the terms included in the LICENSE file.\n\n## Author\n\nNikolaos Tsopanidis (aivc24022)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftsopermon%2Fpannuke-segmentation-aivc-deep-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftsopermon%2Fpannuke-segmentation-aivc-deep-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftsopermon%2Fpannuke-segmentation-aivc-deep-learning/lists"}