Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy labels are often inevitable in the complex manual annotation process, and thus mislead the training of the classification model. In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification. To distinguish the informative hard samples from the harmful noisy ones, we build an easy/hard/noisy (EHN) detection model by using the sample training history. Then we integrate the EHN into a self-training architecture to lower the noise rate through gradually label correction. With the obtained almost clean dataset, we further propose a noise suppressing and hard enhancing (NSHE) scheme to train the noise robust model. Compared with the previous works, our method can save more clean samples and can be directly applied to the real-world noisy dataset scenario without using a clean subset. Experimental results demonstrate that the proposed scheme outperforms the current state-of-the-art methods in both the synthetic and real-world noisy datasets. The source code and data are available at https://github.com/bupt-ai-cz/HSA-NRL/ .

中文翻译：

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用于组织病理学图像分类的硬样本感知噪声鲁棒学习

基于深度学习的组织病理学图像分类是帮助医生提高癌症诊断准确性和及时性的关键技术。然而，在复杂的人工标注过程中，噪声标签往往是不可避免的，从而误导了分类模型的训练。在这项工作中，我们介绍了一种用于组织病理学图像分类的新的硬样本感知噪声鲁棒学习方法。为了区分信息丰富的硬样本和有害的噪声样本，我们通过使用样本训练历史构建了一个简单/硬/噪声（EHN）检测模型。然后我们将 EHN 集成到一个自训练架构中，通过逐步的标签校正来降低噪声率。利用获得的几乎干净的数据集，我们进一步提出了一种噪声抑制和硬增强（NSHE）方案来训练噪声鲁棒模型。与之前的工作相比，我们的方法可以节省更多的干净样本，并且可以直接应用于现实世界的嘈杂数据集场景，而无需使用干净的子集。实验结果表明，所提出的方案在合成和现实世界噪声数据集中都优于当前最先进的方法。源代码和数据可在https://github.com/bupt-ai-cz/HSA-NRL/ .