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：自动医学图像分类是智能诊断系统中的关键组成部分。然而，大多数医学图像数据集包含大量常见疾病的样本，而罕见疾病的样本极少，导致严重的类别不平衡问题。当前，如何从不平衡的训练数据中有效学习是智能诊断领域的一个开放性问题。本文提出一种简单而有效的框架，命名为**基于病灶区域的类别注意力**（CARE），通过在**卷积神经网络**（CNN）的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有助于CNN关注罕见疾病的病灶区域，从而帮助CNN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，且不改变原始网络的结构，因此可直接与任何现有的CNN架构结合使用。CARE框架需要边界框来表示罕见疾病的病灶区域。为减轻手动标注的需求，我们进一步开发了CARE的变体，利用传统的显著性方法或预训练的分割模型来生成边界框。结果表明，采用自动边界框生成的CARE变体与采用*手动*边界框标注的原始CARE框架性能相当。在不平衡的皮肤图像数据集和肺炎数据集上进行的一系列实验表明，我们的方法能有效帮助网络关注罕见疾病的病灶区域，并显著提升罕见疾病的分类性能。