深度学习方法往往受到数据不平衡和数据需求量大的问题的约束。在医学影像学中，恶性或罕见疾病在数据集中往往是少数类，其分布多样。此外，不足的标签和未知的病例也给少数类的训练带来了困惑。为了应对这些问题，我们提出了一种新的层次实例对比学习（HCLe）方法，通过只使用训练阶段中来自多数类的数据来进行少数类检测。为了解决多数类中不一致的内部分布，我们的方法引入了两个分支，其中第一个分支采用了一个自编码器网络，并增加了三个约束函数来有效提取图像级特征，而第二个分支设计了一个新的对比学习网络，其中考虑了来自多数类的层次样本之间的特征一致性。所提出的方法还通过多样的小批量策略进行了进一步的改进，能够在多种条件下识别出少数类。我们进行了大量实验证明了所提出方法在三个具有不同疾病和模态的数据集上的优越性能。实验结果表明，所提出的方法优于现有方法。

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes. To confront the stated problems, we propose a novel Hierarchical-instance Contrastive Learning (HCLe) method for minority detection by only involving data from the majority class in the training stage. To tackle inconsistent intra-class distribution in majority classes, our method introduces two branches, where the first branch employs an auto-encoder network augmented with three constraint functions to effectively extract image-level features, and the second branch designs a novel contrastive learning network by taking into account the consistency of features among hierarchical samples from majority classes. The proposed method is further refined with a diverse mini-batch strategy, enabling the identification of minority classes under multiple conditions. Extensive experiments have been conducted to evaluate the proposed method on three datasets of different diseases and modalities. The experimental results show that the proposed method outperforms the state-of-the-art methods.