H&E whole slide images to predict metastasis of lymph node in T1 colorectal cancer using endoscopically resected specimens.
Notice: This repository is under construction, the full repository will be completed soon.
- python >= 3.6
- numpy >=1.17.4
- openslide-python >= 1.1.2
- pandas >= 1.1.3
- scikit-image >= 0.15.0
- scikit-learn >= 0.23.2
- torch >= 1.5.1 (https://pytorch.org/)
- torchvision >= 0.6.1
- openslide >= 3.4.1 (https://openslide.org/)
- python make_image_list_dict.py
- prepare training and test data
- python train_patch_image.py
- train patch-level image feature extractor
- python test_patch_image.py
- test patch-level image LNM prediction
- python train_slide.py
- train slide-level end-to-end LNM prediction model
- python test_slide.py
- test slide-level LNM prediction
- python show_attention_map.py
- show attention map of the predicted slide
"Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict metastasis of lymph node in T1 colorectal cancer using endoscopically resected specimens" -> paper is under review