Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification

TL;DR

This paper introduces an iterative learning framework with uncertainty sampling and re-weighting strategies to improve medical image classification accuracy despite noisy labels, validated on skin lesion data.

Contribution

It presents a novel framework combining online uncertainty sampling and sample re-weighting to handle noisy labels in medical image classification.

Findings

Achieved promising results on skin lesion classification.

Effectively mitigated the impact of noisy labels.

Enhanced classifier robustness in medical imaging.

Abstract

Deep neural networks (DNNs) have achieved great success in a wide variety of medical image analysis tasks. However, these achievements indispensably rely on the accurately-annotated datasets. If with the noisy-labeled images, the training procedure will immediately encounter difficulties, leading to a suboptimal classifier. This problem is even more crucial in the medical field, given that the annotation quality requires great expertise. In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data. Specifically, an online uncertainty sample mining method is proposed to eliminate the disturbance from noisy-labeled images. Next, we design a sample re-weighting strategy to preserve the usefulness of correctly-labeled hard samples. Our proposed method is validated on skin lesion classification task, and achieved very promising results.