# Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification

arXiv: https://arxiv.org/abs/2406.12293 · HTML: https://mlsanity.com/paper/2406.12293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 58 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Early-Learning Regularization Prevents Memorization of Noisy Labels](https://mlsanity.com/paper/2007.00151/) (2020)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion](https://mlsanity.com/paper/1902.03680/) (2019)
- [Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?](https://mlsanity.com/paper/2209.03302/) (2022)
- [Iterative Learning with Open-set Noisy Labels](https://mlsanity.com/paper/1804.00092/) (2018)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Jo-SRC: A Contrastive Approach for Combating Noisy Labels](https://mlsanity.com/paper/2103.13029/) (2021)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning](https://mlsanity.com/paper/2203.14542/) (2022)
- [Open-set Label Noise Can Improve Robustness Against Inherent Label Noise](https://mlsanity.com/paper/2106.10891/) (2021)
- [Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation](https://mlsanity.com/paper/2206.02791/) (2022)
- [Hard Sample Aware Noise Robust Learning for Histopathology Image Classification](https://mlsanity.com/paper/2112.03694/) (2021)
- [Asymmetric Loss Functions for Learning with Noisy Labels](https://mlsanity.com/paper/2106.03110/) (2021)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels](https://mlsanity.com/paper/2011.05704/) (2020)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Salvage Reusable Samples from Noisy Data for Robust Learning](https://mlsanity.com/paper/2008.02427/) (2020)
- [Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction](https://mlsanity.com/paper/2109.05159/) (2021)
- [Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2104.11436/) (2021)
- [Is your noise correction noisy? PLS: Robustness to label noise with two stage detection](https://mlsanity.com/paper/2210.04578/) (2022)
- [Instance-dependent Label Distribution Estimation for Learning with Label Noise](https://mlsanity.com/paper/2212.08380/) (2022)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook](https://mlsanity.com/paper/2512.09315/) (2025)

## Abstract (arXiv)

Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge. Unlike natural image classification, which often separates and processes closed-set and open-set noisy samples from clean ones, medical image classification contends with high inter-class similarity, complicating the identification of open-set noisy samples. Additionally, existing methods often fail to fully utilize open-set noisy samples for label noise mitigation, leading to their exclusion or the application of uniform soft labels. To address these challenges, we propose the Extended Noise-robust Contrastive and Open-set Feature Augmentation framework for medical image classification tasks. This framework incorporates the Extended Noise-robust Supervised Contrastive Loss, which helps differentiate features among both in-distribution and out-of-distribution classes. This loss treats open-set noisy samples as an extended class, improving label noise mitigation by weighting contrastive pairs according to label reliability. Additionally, we develop the Open-set Feature Augmentation module that enriches open-set samples at the feature level and then assigns them dynamic class labels, thereby leveraging the model's capacity and reducing overfitting to noisy data. We evaluated the proposed framework on both a synthetic noisy dataset and a real-world noisy dataset. The results indicate the superiority of our framework over four existing methods and the effectiveness of leveraging open-set noisy samples to combat label noise.