# Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

arXiv: https://arxiv.org/abs/2109.05159 · HTML: https://mlsanity.com/paper/2109.05159/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [Crowd disagreement about medical images is informative](https://mlsanity.com/paper/1806.08174/) (2018)

## Built on by (most cited first, 16 of 16)

- [Deep learning with noisy labels in medical prediction problems: a scoping review](https://mlsanity.com/paper/2403.13111/) (2024)
- [Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining](https://mlsanity.com/paper/2308.04551/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis](https://mlsanity.com/paper/2207.00807/) (2022)
- [Label-noise-tolerant medical image classification via self-attention and self-supervised learning](https://mlsanity.com/paper/2306.09718/) (2023)
- [Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise](https://mlsanity.com/paper/2407.05973/) (2024)
- [How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?](https://mlsanity.com/paper/2401.07990/) (2024)
- [Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification](https://mlsanity.com/paper/2406.12293/) (2024)
- [EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels](https://mlsanity.com/paper/2510.12687/) (2025)
- [Agreement or Disagreement in Noise-tolerant Mutual Learning?](https://mlsanity.com/paper/2203.15317/) (2022)
- [Dual-Correction Adaptation Network for Noisy Knowledge Transfer](https://mlsanity.com/paper/2207.04423/) (2022)
- [Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification](https://mlsanity.com/paper/2308.12737/) (2023)
- [Sample selection with noise rate estimation in noise learning of medical image analysis](https://mlsanity.com/paper/2312.15233/) (2023)
- [Coordinated Sparse Recovery of Label Noise](https://mlsanity.com/paper/2404.04800/) (2024)
- [MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images](https://mlsanity.com/paper/2505.18741/) (2025)
- [Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning](https://mlsanity.com/paper/2510.08393/) (2025)

## Abstract (arXiv)

With the development of deep learning, medical image classification has been significantly improved. However, deep learning requires massive data with labels. While labeling the samples by human experts is expensive and time-consuming, collecting labels from crowd-sourcing suffers from the noises which may degenerate the accuracy of classifiers. Therefore, approaches that can effectively handle label noises are highly desired. Unfortunately, recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image. To fill the gap, this paper proposes a noise-tolerant medical image classification framework named Co-Correcting, which significantly improves classification accuracy and obtains more accurate labels through dual-network mutual learning, label probability estimation, and curriculum label correcting. On two representative medical image datasets and the MNIST dataset, we test six latest Learning-with-Noisy-Labels methods and conduct comparative studies. The experiments show that Co-Correcting achieves the best accuracy and generalization under different noise ratios in various tasks. Our project can be found at: https://github.com/JiarunLiu/Co-Correcting.