用有序噪声标签提升医学图像严重程度评估准确率
Self-Relaxed Joint Training: Sample Selection for Severity Estimation with Ordinal Noisy Labels
- 利用软标签与硬标签协同选择干净样本
- 在三种疾病数据集上显著优于现有方法
- 适合医疗影像中标签不精准的场景
严重程度估计在医学图像诊断中至关重要，但人工标注成本高且易出错，导致标签噪声普遍。本文提出一种基于有序噪声标签的新训练框架，利用严重程度等级的序数关系来缓解标签噪声影响。方法结合清洁样本选择与双网络架构，通过从噪声硬标签生成软标签，有效提升样本筛选精度和模型鲁棒性。在两种内镜溃疡性结肠炎（UC）数据集和一个视网膜糖尿病视网膜病变（DR）数据集上的实验表明，该方法显著优于多种先进方法。代码已开源：https://github.com/shumpei-takezaki/Self-Relaxed-Joint-Training。
原文摘要 · Abstract (English)
Severity level estimation is a crucial task in medical image diagnosis. However, accurately assigning severity class labels to individual images is very costly and challenging. Consequently, the attached labels tend to be noisy. In this paper, we propose a new framework for training with ``ordinal'' noisy labels. Since severity levels have an ordinal relationship, we can leverage this to train a classifier while mitigating the negative effects of noisy labels. Our framework uses two techniques: clean sample selection and dual-network architecture. A technical highlight of our approach is the use of soft labels derived from noisy hard labels. By appropriately using the soft and hard labels in the two techniques, we achieve more accurate sample selection and robust network training. The proposed method outperforms various state-of-the-art methods in experiments using two endoscopic ulcerative colitis (UC) datasets and a retinal Diabetic Retinopathy (DR) dataset. Our codes are available at https://github.com/shumpei-takezaki/Self-Relaxed-Joint-Training.
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