Pytorch implementation of NEGEV method. Paper: "Negative Evidence Matters in Interpretable Histology Image Classification".

Related tags

Deep Learning deep-learning weakly-supervised-learning interpretability histology-images
Overview

Pytorch 1.10.0 code for:

Negative Evidence Matters in Interpretable Histology Image Classification (https://arxiv. org/abs/xxxx.xxxxx)

Citation:

@article{negevsbelharbi2021,
  title={Negative Evidence Matters  in Interpretable Histology Image Classification},
  author={Belharbi, S. and  Pedersoli, M and
  Ben Ayed, I. and McCaffrey, L. and Granger, E.},
  journal={CoRR},
  volume={abs/xxxx.xxxxx},
  year={2021}
}

Issues:

Please create a github issue.

Content:

Method:

method

Results:

glas-results

camelyon16-results

Requirements:

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:

2.2. Download datasets:

You find the splits in ./folds.

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
  • NEGEV-method: 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 NEGEV \
                         --world_size 1 \
                         --task NEGEV \
                         --encoder_name resnet50 \
                         --arch UnetNEGEV \
                         --runmode final-mode \
                         --opt__name_optimizer sgd \
                         --dist_backend mpi \
                         --batch_size 32 \
                         --eval_checkpoint_type best_localization \
                         --opt__step_size 250 \
                         --opt__gamma 0.1 \
                         --max_epochs 1000 \
                         --freeze_cl True \
                         --support_background True \
                         --method CAM \
                         --spatial_pooling WGAP \
                         --dataset GLAS \
                         --fold 0 \
                         --cudaid 0 \
                         --debug_subfolder None \
                         --amp True \
                         --opt__lr 0.1 \
                         --negev_ptretrained_cl_cp best_localization \
                         --elb_init_t 1.0 \
                         --elb_max_t 10.0 \
                         --elb_mulcoef 1.01 \
                         --sl_ng True \
                         --sl_ng_seeder probability_seeder \
                         --sl_ng_lambda 1.0 \
                         --sl_ng_start_ep 0 \
                         --sl_ng_end_ep -1 \
                         --sl_ng_min 1 \
                         --sl_ng_max 1 \
                         --sl_ng_ksz 3 \
                         --crf_ng False \
                         --jcrf_ng False \
                         --neg_samples_ng False \
                         --max_sizepos_ng False \
                         --exp_id 12_13_2021_00_49_48_796469__3314599
  • Train the CAM-method first. Then, copy the best model from the exp folder into the folder ./pretrained. Copy the whole folder with this name format GLAS-0-resnet50-CAM-WGAP-cp_best_localization.
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Comments
  • I couldn't reach your results in paper

    I couldn't reach your results in paper

    Thanks for sharing your code and doning valuable research in WSOL for histology images. I ran your code with exacty the same hyper-parameters as you mentioned on github, but I couldn't reach your results in paper. In paper, PxAP performance of NEGEV approach with ResNet50 on GlaS dataset is reported 82.0. However, I reached the following result for BEST_LOCALIZATION:

    DLL 2022-12-01 17:46:32.010136 - 1:00:49.452464 REPORT EPOCH/74: split: test/metric classification: 98.75 DLL 2022-12-01 17:46:32.010156 - 1:00:49.452484 REPORT EPOCH/74: split: test/metric classification: 98.75_best DLL 2022-12-01 17:46:32.010178 - 1:00:49.452506 REPORT EPOCH/74: split: test/metric localization: 78.56850533742725 DLL 2022-12-01 17:46:32.010198 - 1:00:49.452526 REPORT EPOCH/74: split: test/metric localization: 78.56850533742725_best DLL 2022-12-01 17:46:32.010237 - 1:00:49.452565 REPORT EPOCH/74 split: test: [BEST] PXAP: 78.56850533742725 True positive: 61.31941981950956 False negative: 38.68058018049044 True negative: 77.9749846417977 False positive: 22.0250153582023 Dice foreground: 67.2735510339999 Dice background: 71.34570169405362 MIOU: 53.07061235795351 Best tau: [0.5650000000000001]

    Let me know if I missed something.

    opened by Reza-Safdari 1
Owner
Soufiane Belharbi
Post-doc at LIVIA Lab. ÉTS Montreal, in collab. with McCaffrey Lab. /GCRC McGill. Training neural networks with weak supervision.
Soufiane Belharbi
GitHub
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