survey_wsl_histology

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Pytorch 1.10.0 code for:

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey. Webpage, PDF

Poster MIDL 2023

Citation:

@article{rony2023deep,
    title ={Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey},
    author={Rony, J. and Belharbi, S. and Dolz, J. and Ben Ayed, I. and McCaffrey, L. and Granger, E.},
    journal = "Machine Learning for Biomedical Imaging",
    volume = "2",
    year = "2023",
    pages = "96--150"
}

Issues:

Please create a github issue.

Content:

• View
• Results
• Requirements
• Datasets
• Run code

Method:

Installed methods:

• GAP
• Max-Pooling
• LSE
• CAM
• HaS
• WILDCAT
• ACoL
• SPG
• Deep MIL
• PRM
• ADL
• CutMix
• TS-CAM
• MAXMIN
• NEGEV
• GradCAM
• GradCAM++
• Smooth-GradCAM++
• LayerCAM
• U-Net

Results:

Requirements:

• Python 3.7.10
• Pytorch 1.10.0
• torchvision 0.11.1

pip install torch==1.10.0 -f https://download.pytorch.org/whl/cu111/torch-1.10.0%2Bcu111-cp37-cp37m-linux_x86_64.whl
pip install torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torchvision-0.11.1%2Bcu111-cp37-cp37m-linux_x86_64.whl

• Full dependencies
• Build and install CRF:
  • Install Swig
  • CRF (not used in this work, but it is part of the code.)

cdir=$(pwd)
cd dlib/crf/crfwrapper/bilateralfilter
swig -python -c++ bilateralfilter.i
python setup.py install
cd $cdir
cd dlib/crf/crfwrapper/colorbilateralfilter
swig -python -c++ colorbilateralfilter.i
python setup.py install

Download datasets :

2.1. Links to dataset:

• GlaS
• Camelyon16

2.2. Download datasets:

• GlaS: ./download-glas-dataset.sh.

You find the splits in ./folds.

2.3 Code for datasets split/sampling (+ patches sampling from WSI):

• See datasets-split.
• Detailed documentation: datasets-split/README.md.

Run code :

• CAM-method: CAM over GLAS using ResNet50:

cudaid=$1
export CUDA_VISIBLE_DEVICES=$cudaid
getfreeport() {
freeport=$(python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
}
export OMP_NUM_THREADS=50
export NCCL_BLOCKING_WAIT=1
plaunch=$(python -c "from os import path; import torch; print(path.join(path.dirname(torch.__file__), 'distributed', 'launch.py'))")
getfreeport
torchrun --nnodes=1 --node_rank=0 --nproc_per_node=1  \
                         --master_port=$freeport main_wsol.py \ --local_world_size=1 \
                         --task STD_CL \
                         --encoder_name resnet50 \
                         --arch STDClassifier \
                         --runmode final-mode \
                         --opt__name_optimizer sgd \
                         --batch_size 32 \
                         --eval_checkpoint_type best_localization \
                         --opt__step_size 250 \
                         --opt__gamma 0.1 \
                         --max_epochs 1000 \
                         --freeze_cl False \
                         --support_background True \
                         --method CAM \
                         --spatial_pooling WGAP \
                         --dataset GLAS \
                         --fold 0 \
                         --cudaid 0 \
                         --debug_subfolder None \
                         --amp True \
                         --opt__lr 0.003 \
                         --exp_id 11_19_2021_09_32_36_109051__423849