Accurate brain tumor classification is critical for intra-operative decision making in neuro-oncological surgery. However, existing approaches are restricted to a fixed set of predefined classes and are therefore unable to capture patterns of tumor types not available during training. Unsupervised learning can extract general-purpose features, but it lacks the ability to incorporate prior knowledge from labelled data, and semi-supervised methods often assume that all potential classes are represented in the labelled data. Generalized Category Discovery (GCD) aims to bridge this gap by categorizing both known and unknown classes within unlabelled data. To reflect the hierarchical structure of brain tumor taxonomies, in this work, we introduce Hierarchical Generalized Category Discovery for Brain Tumor Classification (HGCD-BT), a novel approach that integrates hierarchical clustering with contrastive learning. Our method extends contrastive learning based GCD by incorporating a novel semi-supervised hierarchical clustering loss. We evaluate HGCD-BT on OpenSRH, a dataset of stimulated Raman histology brain tumor images, achieving a +28% improvement in accuracy over state-of-the-art GCD methods for patch-level classification, particularly in identifying previously unseen tumor categories. Furthermore, we demonstrate the generalizability of HGCD-BT on slide-level classification of hematoxylin and eosin stained whole-slide images from the Digital Brain Tumor Atlas, confirming its utility across imaging modalities.

翻译：脑肿瘤的准确分类对于神经肿瘤学手术中的术中决策至关重要。然而，现有方法局限于一组固定的预定义类别，因此无法捕捉训练期间未出现的肿瘤类型模式。无监督学习可以提取通用特征，但它缺乏整合来自标记数据的先验知识的能力，而半监督方法通常假设所有潜在类别都在标记数据中有所体现。广义类别发现旨在通过将未标记数据中的已知和未知类别进行分类来弥合这一差距。为了反映脑肿瘤分类学的层次结构，在本工作中，我们引入了用于脑肿瘤分类的分层广义类别发现，这是一种将分层聚类与对比学习相结合的新方法。我们的方法通过引入一种新颖的半监督分层聚类损失，扩展了基于对比学习的广义类别发现。我们在OpenSRH（一个受激拉曼组织学脑肿瘤图像数据集）上评估了该方法，在图像块级别分类上，相较于最先进的广义类别发现方法，准确率提高了28%，尤其是在识别先前未见过的肿瘤类别方面。此外，我们在数字脑肿瘤图谱的苏木精-伊红染色全玻片图像的玻片级别分类上验证了该方法的泛化能力，证实了其在不同成像模态间的实用性。