IEEE Trans Med Imaging. 2022 Dec;41(12):3533-3543. doi: 10.1109/TMI.2022.3188326. Epub 2022 Dec 2.
Histopathology image classification plays a critical role in clinical diagnosis. However, due to the absence of clinical interpretability, most existing image-level classifiers remain impractical. To acquire the essential interpretability, lesion-level diagnosis is also provided, relying on detailed lesion-level annotations. Although the multiple-instance learning (MIL)-based approach can identify lesions by only utilizing image-level annotations, it requires overly strict prior information and has limited accuracy in lesion-level tasks. Here, we present a novel severity-guided multiple instance curriculum learning (Su-MICL) strategy to avoid tedious labeling. The proposed Su-MICL is under a MIL framework with a neglected prior: disease severity to define the learning difficulty of training images. Based on the difficulty degree, a curriculum is developed to train a model utilizing images from easy to hard. The experimental results for two histopathology image datasets demonstrate that Su-MICL achieves comparable performance to the state-of-the-art weakly supervised methods for image-level classification, and its performance for identifying lesions is closest to the supervised learning method. Without tedious lesion labeling, the Su-MICL approach can provide an interpretable diagnosis, as well as an effective insight to aid histopathology image diagnosis.
组织病理学图像分类在临床诊断中起着至关重要的作用。然而，由于缺乏临床可解释性，大多数现有的图像级别分类器仍然不切实际。为了获得必要的可解释性，还提供了病变级别诊断，依赖于详细的病变级别注释。虽然基于多实例学习（MIL）的方法仅利用图像级别注释就可以识别病变，但它需要过于严格的先验信息，并且在病变级别任务中的准确性有限。在这里，我们提出了一种新的基于严重程度指导的多实例课程学习（Su-MICL）策略来避免繁琐的标注。所提出的 Su-MICL 是在忽略先验信息的 MIL 框架下提出的：疾病严重程度来定义训练图像的学习难度。基于难度等级，开发了一个课程来利用从简单到困难的图像训练模型。在两个组织病理学图像数据集上的实验结果表明，Su-MICL 在图像级别分类方面的性能可与最先进的弱监督方法相媲美，并且其识别病变的性能最接近监督学习方法。无需繁琐的病变标注，Su-MICL 方法不仅可以提供可解释的诊断，还可以为辅助组织病理学图像诊断提供有效的见解。
