Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named \textbf{C}lass \textbf{A}ttention to \textbf{RE}gions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of \textbf{C}onvolutional \textbf{N}eural \textbf{N}etworks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with \textit{manual} bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases.

翻译：自动化医学图像分类是智能诊断系统的关键组成部分。然而，大多数医学图像数据集包含大量常见疾病样本，仅少量罕见疾病样本，导致严重的类别不平衡问题。当前，如何从不平衡训练数据中有效学习是智能诊断领域的一个开放性问题。本文提出一个简单而有效的框架，名为类别关注病变区域（CARE），通过在卷积神经网络（CNN）训练过程中嵌入注意力机制来处理数据不平衡问题。所提出的注意力模块帮助CNN关注罕见疾病的病变区域，从而更有效地学习其特征。此外，该注意力模块仅在训练阶段工作，不改变原始网络架构，因此可直接与任何现有CNN架构结合。CARE框架需要边界框来表示罕见疾病的病变区域。为减少人工标注需求，我们进一步开发了CARE的变体，利用传统显著性方法或预训练分割模型生成边界框。结果表明，采用自动生成边界框的CARE变体与使用人工边界框标注的原始CARE框架性能相当。在不平衡皮肤图像数据集和肺炎数据集上的一系列实验表明，我们的方法能有效帮助网络聚焦于罕见疾病的病变区域，并显著提升罕见疾病的分类性能。