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

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.
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
BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization

BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization Authors: Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong,

Salesforce 125 Dec 31, 2022
Yolov5-opencv-cpp-python - Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

yolov5-opencv-cpp-python Example of performing inference with ultralytics YOLO V

183 Jan 09, 2023
Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

personalized-breath Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality". Guideline To ex

Manh-Ha Bui 2 Nov 15, 2021
Calling Julia from Python - an experiment on data loading

Calling Julia from Python - an experiment on data loading See the slides. TLDR After reading Patrick's blog post, we decided to try to replace C++ wit

Abel Siqueira 8 Jun 07, 2022
FinEAS: Financial Embedding Analysis of Sentiment 📈

FinEAS: Financial Embedding Analysis of Sentiment 📈 (SentenceBERT for Financial News Sentiment Regression) This repository contains the code for gene

LHF Labs 31 Dec 13, 2022
This repo contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021.

Off-Belief Learning Introduction This repo contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021. Environment Setup

Facebook Research 32 Jan 05, 2023
TabNet for fastai

TabNet for fastai This is an adaptation of TabNet (Attention-based network for tabular data) for fastai (=2.0) library. The original paper https://ar

Mikhail Grankin 116 Oct 21, 2022
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

Deep learning based state estimation: incorporating Transformer and LSTM to Kalman Filter with EM algorithm Overview Kalman Filter requires the true p

zshicode 57 Dec 27, 2022
PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)

PSTR (CVPR2022) This code is an official implementation of "PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)". End-to-end one-step

Jiale Cao 28 Dec 13, 2022
Direct design of biquad filter cascades with deep learning by sampling random polynomials.

IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe

Christian J. Steinmetz 55 Nov 02, 2022
Dilated RNNs in pytorch

PyTorch Dilated Recurrent Neural Networks PyTorch implementation of Dilated Recurrent Neural Networks (DilatedRNN). Getting Started Installation: $ pi

Zalando Research 200 Nov 17, 2022
SGoLAM - Simultaneous Goal Localization and Mapping

SGoLAM - Simultaneous Goal Localization and Mapping PyTorch implementation of the MultiON runner-up entry, SGoLAM: Simultaneous Goal Localization and

10 Jan 05, 2023
Bravia core script for python

Bravia-Core-Script You need to have a mandatory account If this L3 does not work, try another L3. enjoy

5 Dec 26, 2021
A Keras implementation of YOLOv3 (Tensorflow backend)

keras-yolo3 Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. Quick Start Download YOLOv3 weights fro

7.1k Jan 03, 2023
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions This repo contains the dataset and code for the paper Benchmarking Ro

Jiachen Sun 168 Dec 29, 2022
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Text-AutoAugment (TAA) This repository contains the code for our paper Text AutoAugment: Learning Compositional Augmentation Policy for Text Classific

LancoPKU 105 Jan 03, 2023
MaskTrackRCNN for video instance segmentation based on mmdetection

MaskTrackRCNN for video instance segmentation Introduction This repo serves as the official code release of the MaskTrackRCNN model for video instance

411 Jan 05, 2023
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Dec 31, 2022
Self-supervised learning optimally robust representations for domain generalization.

OptDom: Learning Optimal Representations for Domain Generalization This repository contains the official implementation for Optimal Representations fo

Yangjun Ruan 18 Aug 25, 2022