Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sample imbalance among different diseases leads algorithms biased to the majority categories, leading to poor performance for rare categories. Existing works formulated this challenge as a long-tailed problem and attempted to tackle it by decoupling the feature representation and classification. Yet, due to the imbalanced distribution and limited samples from tail classes, these works are prone to biased representation learning and insufficient classifier calibration. To tackle these problems, we propose a new Long-tailed Medical Diagnosis (LMD) framework for balanced medical image classification on long-tailed datasets. In the initial stage, we develop a Relation-aware Representation Learning (RRL) scheme to boost the representation ability by encouraging the encoder to capture intrinsic semantic features through different data augmentations. In the subsequent stage, we propose an Iterative Classifier Calibration (ICC) scheme to calibrate the classifier iteratively. This is achieved by generating a large number of balanced virtual features and fine-tuning the encoder using an Expectation-Maximization manner. The proposed ICC compensates for minority categories to facilitate unbiased classifier optimization while maintaining the diagnostic knowledge in majority classes. Comprehensive experiments on three public long-tailed medical datasets demonstrate that our LMD framework significantly surpasses state-of-the-art approaches. The source code can be accessed at https://github.com/peterlipan/LMD.

翻译：近年来，计算机辅助诊断展现出令人瞩目的性能，有效减轻了临床医生的工作负担。然而，不同疾病间固有的样本不平衡导致算法偏向多数类别，使得罕见类别的诊断性能不佳。现有研究将这一挑战建模为长尾问题，并尝试通过解耦特征表示与分类来解决。然而，由于分布不平衡及尾部类别样本有限，这些方法容易产生有偏的表示学习和不足的分类器校准。为解决这些问题，我们提出一种新的长尾医疗诊断框架，用于在长尾数据集上实现平衡的医学图像分类。在初始阶段，我们设计了关系感知表示学习方案，通过鼓励编码器利用不同数据增强捕捉内在语义特征，以提升表示能力。在后续阶段，我们提出迭代分类器校准方案，通过生成大量平衡的虚拟特征并以期望最大化方式微调编码器，实现分类器的迭代校准。该方案通过补偿少数类别促进无偏分类器优化，同时保留多数类别的诊断知识。在三个公开长尾医疗数据集上的综合实验表明，我们的LMD框架显著超越了现有最优方法。源代码可通过 https://github.com/peterlipan/LMD 获取。