Deep Learning Utilities for Pathology: PyTorch-lightning implementation of multiple instance learning models
==== WORK IN PROGRESS ====
- Add bash scripts to run compilation of features from VISSL
- Add bash scripts to run training of all classifiers
- Add config files
- Improve documentation
==========================
dlup_lightning_mil offers a set MIL classification models built with PyTorch-lightning using utilities from DLUP to ease the process of running Deep Learning classification/regression models on Whole Slide Images.
Tile supervision methods can be run with just this repository.
MIL methods require feature extraction using hissl.
The scripts in ~/dlup-lightning-mil/tools/reproduce_deepsmile will reproduce the models
as described in DeepSMILE. It is expected you run ~/hissl/tools/reproduce_deepsmile/reproduce_deepsmile.sh from hissl on
the same machine and hosting the repository in the same parent directory.
Running ~/dlup-lightning-mil/tools/reproduce_deepsmile$ bash reproduce_deepsmile.sh
will run the following scripts:
0_check_singularity/check_singularity.sh- Checks if a singularity image is available to run all following scripts
1_create_splits_for_tcga_crck- Creates 5-fold train-val splits for TCGA-CRCk from the defined train set
- Creates subsets of the training splits for low labelled data regime experiments
2_compile_features/compile_h5_features.sh- Reorganizes the saved features from
hisslto allow for easier and quicker loading
- Reorganizes the saved features from
3_train_models/*- Runs
- Every combination of extractor and classifier
- On both datasets
- For all available labels
- For all defined fractions of training data
- Saves to
~/dlup-lightning-mil/logs- model checkpoints
- training loss
- validation and test AUCs
- validation and test predictions and labels
- MIL attention or predicted score for each tile
- Runs
- Analysis of results of runs
- Visualization of high- and low-attention tiles
- Plotting of graphs
- More MIL models & end-to-end MIL training