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

TL;DR

This paper introduces a novel framework that effectively utilizes open-set noisy samples in medical image classification, improving label noise mitigation by differentiating features and augmenting open-set samples at the feature level.

Contribution

The proposed framework uniquely incorporates an extended contrastive loss and feature augmentation to leverage open-set noisy samples, addressing challenges specific to medical imaging.

Findings

Outperforms four existing methods on noisy datasets

Effectively differentiates in-distribution and out-of-distribution features

Leverages open-set noisy samples to reduce label noise impact

Abstract

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.