#Confident_Learning_for_Noisy-labeled_Medical_Image_Segmentation

This repository is for 2D Noisy-labeled Medical Image Segmentation with Confident Learning introduced by the following paper

Minqing Zhang, Jiantao Gao, Zhen Lyu, Weibing Zhao, Qin Wang, Weizhen Ding, Sheng Wang, Zhen Li* and Shuguang Cui, "Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation", MICCAI 2020. Paper

Please consider citing this paper if it offered help in your work.

Zhang M, Gao J, Lyu Z, et al. Characterizing Label Errors: Confident Learning for Noisy-Labeled Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2020: 721-730.

All of the experiments reported in the paper were conducted under the following configuration. Other configurations might not be guaranteed feasible.

Ubuntu 16.04.5
CUDA 10.0.130
Pytorch 1.2.0

This project comprises of 10 folders and 2 scripts, and each of which is going to be described in the following

/common : general interfaces like model saving
/config : configurations related to training models
/dataset : dataset implement according to pytorch
/jsrt_data : the original JSRT chest X-ray image dataset utilized to conduct our experiments
/logger : code involved with training logging
/loss : loss functions
/metrics : metrics like dice-coefficient
/models : saving models
/net : network architectures
/utils : scripts involved with synthesizing noisy-labeled datasets and generating confident maps

train_pixel_level_classification.py : segmentation model training
test_pixel_level_classification.py : testing a model

1. synthesizing a noisy-labeled dataset with the script utils/noisy_dataset_generation_test.py (three variables: alpha, class_name and beta need to be specified, refering to our paper for more implementation details)
   
2. preparing for teacher model training by specified settings in config/config_confident_learning_pixel_level_classification.py
   
3. training teacher models with the script train_pixel_level_classification.py
   
4. characterizing label errors with the script utils/confident_map_generation_test.py
   
5. preparing for student model training by specified settings in config/config_confident_learning_pixel_level_classification.py
   
6. training a student model with the script train_pixel_level_classification.py
   
7. testing the student model with the script test_pixel_level_classification.py