pathology-whole-slide-learning

Framework for weak whole slide learning with attention: compress and classify whole slide images. Requires ASAP and the wholeslidedata package and optionally pathology-whole-slide-packer.

Preprocessing: For slides bigger then ~50k pixels in height/width packing is recommended, as otherwise the gpu out of memory might occur.

Steps:

1. Compress the slides
2. Train Classifier
3. Evaluate/apply classifier

For compression several pretrained encoders are supported, i.e.

• res50: an ImageNet pretrained ResNet50
• mtdp_res50: Histologically pretrained encoder by Mormont et al. [1] Requires the multitask-dipath library to be present in the PYTHONPATH (https://github.com/waliens/multitask-dipath)
• histossl: Histologically pretrained ResNet18 encoder by Ciga et al. [2]. Requires to download their model to ~/.torch/models

Compression script: wsilearn.compress.compress.py

Classification script: wsilearn.train_nic_pl.py

More documentation to follow soon

[1] Mormont, Romain, Pierre Geurts, and Raphaël Marée. "Multi-task pre-training of deep neural networks for digital pathology." IEEE journal of biomedical and health informatics 25.2 (2020): 412-421

[2] Ciga, Ozan, Tony Xu, and Anne Louise Martel. "Self supervised contrastive learning for digital histopathology." Machine Learning with Applications 7 (2022): 100198.