https://github.com/tsopermon/pannuke-segmentation-aivc-deep-learning
deep learning msc assignment
https://github.com/tsopermon/pannuke-segmentation-aivc-deep-learning
deeplab-v3-plus pannuke resnet-50 unet-image-segmentation
Last synced: 9 months ago
JSON representation
deep learning msc assignment
- Host: GitHub
- URL: https://github.com/tsopermon/pannuke-segmentation-aivc-deep-learning
- Owner: tSopermon
- License: mit
- Created: 2025-08-31T12:44:49.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-31T12:52:52.000Z (10 months ago)
- Last Synced: 2025-08-31T14:38:51.542Z (10 months ago)
- Topics: deeplab-v3-plus, pannuke, resnet-50, unet-image-segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 7.16 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PanNuke Semantic Segmentation Project
This repository contains implementations of deep learning models for nuclei instance segmentation and classification using the PanNuke dataset. The project explores different architectural approaches including U-Net and DeepLabV3+ with ResNet50 for semantic segmentation tasks in histopathological images.
## Dataset
The PanNuke dataset consists of:
- 189,744 labeled nuclei with instance segmentation masks
- 7,901 images (256×256 pixels)
- 19 tissue types
- 5 cell categories (Neoplastic, Inflammatory, Connective, Dead, Epithelial)
- Images captured at x40 magnification (0.25 µm/pixel resolution)
## Implementation
The project includes three main implementations:
1. Basic U-Net architecture
2. ResNet50 with U-Net
3. ResNet50 with DeepLabV3+
Each implementation is available in separate Jupyter notebooks in the `notebooks/` directory.
## Methods
The implementations utilize state-of-the-art deep learning architectures for semantic segmentation:
- U-Net: Convolutional network architecture specifically designed for biomedical image segmentation
- ResNet50: Deep residual network used as a backbone for feature extraction
- DeepLabV3+: Advanced semantic segmentation architecture incorporating atrous convolutions
## Reference Papers
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
## License
This project is licensed under the terms included in the LICENSE file.
## Author
Nikolaos Tsopanidis (aivc24022)