用自监督预训练和自适应置信损失，提升带噪声眼底图像分类准确率
SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
- 先用MAE自监督预训练提取鲁棒特征，避开标签噪声干扰
- 通过自适应置信阈值和标签松弛，构建可能性分布提升真实标签估计
- 模拟临床真实噪声场景，适合医疗图像中存在标注错误的场景
眼底图像分类在辅助诊断中至关重要，但标签噪声会严重损害深度神经网络性能。为此，我们提出一种鲁棒框架SSP-RACL，用于处理眼底图像数据集中的标签噪声。首先，采用掩码自编码器（MAE）进行预训练以提取不受标签噪声影响的特征；随后，利用超集学习框架，设置置信度阈值与自适应标签松弛参数，构建可能性分布，提供更可靠的真值估计，从而有效抑制模型对噪声标签的过拟合记忆。此外，引入基于临床知识的非对称噪声生成方法，模拟真实世界中的噪声眼底图像数据集。实验结果表明，所提方法在处理标签噪声方面优于现有方法，表现出更优性能。
原文摘要 · Abstract (English)
Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. To address this challenge, we propose a robust framework, Self-Supervised Pre-training with Robust Adaptive Credal Loss (SSP-RACL), for handling label noise in fundus image datasets. First, we use Masked Autoencoders (MAE) for pre-training to extract features, unaffected by label noise. Subsequently, RACL employ a superset learning framework, setting confidence thresholds and adaptive label relaxation parameter to construct possibility distributions and provide more reliable ground-truth estimates, thus effectively suppressing the memorization effect. Additionally, we introduce clinical knowledge-based asymmetric noise generation to simulate real-world noisy fundus image datasets. Experimental results demonstrate that our proposed method outperforms existing approaches in handling label noise, showing superior performance.
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