Clinically, histopathology images always offer a golden standard for disease diagnosis. With the development of artificial intelligence, digital histopathology significantly improves the efficiency of diagnosis. Nevertheless, noisy labels are inevitable in histopathology images, which lead to poor algorithm efficiency. Curriculum learning is one of the typical methods to solve such problems. However, existing curriculum learning methods either fail to measure the training priority between difficult samples and noisy ones or need an extra clean dataset to establish a valid curriculum scheme. Therefore, a new curriculum learning paradigm is designed based on a proposed ranking function, which is named The Ranking Margins (TRM). The ranking function measures the ‘distances’ between samples and decision boundaries, which helps distinguish difficult samples and noisy ones. The proposed method includes three stages: the warm-up stage, the main training stage and the fine-tuning stage. In the warm-up stage, the margin of each sample is obtained through the ranking function. In the main training stage, samples are progressively fed into the networks for training, starting from those with larger margins to those with smaller ones. Label correction is also performed in this stage. In the fine-tuning stage, the networks are retrained on the samples with corrected labels. In addition, we provide theoretical analysis to guarantee the feasibility of TRM. The experiments on two representative histopathologies image datasets show that the proposed method achieves substantial improvements over the latest Label Noise Learning (LNL) methods.

中文翻译：

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通过排名边缘使用噪声标签进行组织病理学图像分类


临床上，组织病理学图像始终为疾病诊断提供黄金标准。随着人工智能的发展，数字组织病理学显着提高了诊断效率。然而，组织病理学图像中不可避免地会出现噪声标签，这导致算法效率较差。课程学习是解决此类问题的典型方法之一。然而，现有的课程学习方法要么无法衡量困难样本和噪声样本之间的训练优先级，要么需要额外的干净数据集来建立有效的课程方案。因此，基于所提出的排名函数设计了一种新的课程学习范式，称为排名利润（TRM）。排序函数测量样本和决策边界之间的“距离”，这有助于区分困难样本和噪声样本。该方法包括三个阶段：热身阶段、主训练阶段和微调阶段。在预热阶段，通过排序函数获得每个样本的margin。在主要训练阶段，样本被逐步输入网络进行训练，从边缘较大的样本开始，到边缘较小的样本。标签校正也在这个阶段进行。在微调阶段，网络在具有正确标签的样本上进行重新训练。此外，我们还提供了理论分析来保证TRM的可行性。在两个代表性组织病理学图像数据集上的实验表明，所提出的方法比最新的标签噪声学习（LNL）方法取得了实质性改进。