Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which presents a significant challenge. While the multi-instance learning (MIL) paradigm alleviates the computational burden of WSIs, existing MIL methods often overlook hierarchical label correlations, treating fine-grained classification as a flat multi-class classification task. To overcome these limitations, we introduce a novel hierarchical multi-instance learning (HMIL) framework. By facilitating on the hierarchical alignment of inherent relationships between different hierarchy of labels at instance and bag level, our approach provides a more structured and informative learning process. Specifically, HMIL incorporates a class-wise attention mechanism that aligns hierarchical information at both the instance and bag levels. Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability for fine-grained classification and a curriculum-based dynamic weighting module to adaptively balance the hierarchical feature during training. Extensive experiments on our large-scale cytology cervical cancer (CCC) dataset and two public histology datasets, BRACS and PANDA, demonstrate the state-of-the-art class-wise and overall performance of our HMIL framework. Our source code is available at https://github.com/ChengJin-git/HMIL.

翻译：全切片图像（WSI）的细粒度分类在精准肿瘤学中至关重要，能够实现精确的癌症诊断和个性化治疗策略。该任务的核心在于区分同一大类千兆像素分辨率图像中细微的形态学差异，这构成了重大挑战。虽然多示例学习（MIL）范式缓解了WSI的计算负担，但现有的MIL方法常常忽略层次化的标签关联，将细粒度分类视为扁平的多元分类任务。为了克服这些限制，我们提出了一种新颖的层次化多示例学习（HMIL）框架。通过促进实例级别和包级别上不同层次标签之间固有关系的层次对齐，我们的方法提供了一个更具结构性和信息量的学习过程。具体而言，HMIL引入了一种类感知注意力机制，在实例和包两个层面实现层次信息的对齐。此外，我们引入了监督对比学习以增强细粒度分类的判别能力，以及一个基于课程学习的动态加权模块，在训练过程中自适应地平衡层次化特征。在我们大规模细胞学宫颈癌（CCC）数据集以及两个公开的组织学数据集BRACS和PANDA上进行的大量实验表明，我们的HMIL框架在类级别和整体性能上均达到了最先进水平。我们的源代码可在 https://github.com/ChengJin-git/HMIL 获取。