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

Last update: Dec 01, 2022

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.

Method:

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:

2.2. Download datasets:

GlaS: ./download-glas-dataset.sh.

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.

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

Related tags

Overview

Pytorch 1.10.0 code for:

Citation:

Issues:

Content:

Method:

Results:

Requirements:

Download datasets :

2.1. Links to dataset:

2.2. Download datasets:

Run code :

Owner

Soufiane Belharbi

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Official repo for SemanticGAN https://nv-tlabs.github.io/semanticGAN/

Face and other object detection using OpenCV and ML Yolo

Differentiable Wavetable Synthesis

Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)

Sound Event Detection with FilterAugment

EgGateWayGetShell py脚本

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

ESL: Event-based Structured Light

HDMapNet: A Local Semantic Map Learning and Evaluation Framework

Dungeons and Dragons randomized content generator

This porject is intented to build the most accurate model for predicting the porbability of loan default

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

Related tags

Overview

Pytorch 1.10.0 code for:

Citation:

Issues:

Content:

Method:

Results:

Requirements:

Download datasets :

2.1. Links to dataset:

2.2. Download datasets:

Run code :

Owner

Soufiane Belharbi

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Official repo for SemanticGAN https://nv-tlabs.github.io/semanticGAN/

Face and other object detection using OpenCV and ML Yolo

Differentiable Wavetable Synthesis

Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)

Sound Event Detection with FilterAugment

EgGateWayGetShell py脚本

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

ESL: Event-based Structured Light

HDMapNet: A Local Semantic Map Learning and Evaluation Framework

Dungeons and Dragons randomized content generator

This porject is intented to build the most accurate model for predicting the porbability of loan default

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI