Abstract
Domain generalization in computational histopathology is hindered by heterogeneity in whole slide images (WSIs), caused by variations in tissue preparation, staining, and imaging conditions across institutions. Unlike machine learning systems, pathologists rely on domain-invariant morphological cues such as nuclear atypia (enlargement, irregular contours, hyperchromasia, chromatin texture, spatial disorganization), structural atypia (abnormal architecture and gland formation), and overall morphological atypia that remain diagnostic across diverse settings. Motivated by this, we hypothesize that explicitly modeling biologically robust nuclear morphology and spatial organization will enable the learning of cancer representations that are resilient to domain shifts. We propose MorphGen (Morphology-Guided Generalization), a method that integrates histopathology images, augmentations, and nuclear segmentation masks within a supervised contrastive learning framework. By aligning latent representations of images and nuclear masks, MorphGen prioritizes diagnostic features such as nuclear and morphological atypia and spatial organization over staining artifacts and domain-specific features. To further enhance out-of-distribution robustness, we incorporate stochastic weight averaging (SWA), steering optimization toward flatter minima. Attention map analyses revealed that MorphGen primarily relies on nuclear morphology, cellular composition, and spatial cell organization within tumors or normal regions for final classification. Finally, we demonstrate resilience of the learned representations to image corruptions (such as staining artifacts) and adversarial attacks, showcasing not only OOD generalization but also addressing critical vulnerabilities in current deep learning systems for digital pathology. Code, datasets, and trained models are available at: this https URL
Abstract (translated)
计算病理学中的领域泛化由于整个切片图像（WSIs）的异质性而受到阻碍，这种异质性是由不同机构在组织准备、染色和成像条件上的差异造成的。与机器学习系统不同，病理学家依赖于跨多种设置仍具有诊断价值的域不变形态线索，例如核异常（增大、不规则轮廓、着色加深、染色质纹理、空间失序）、结构异常（异常架构和腺体形成）以及整体形态学异常。 受此启发，我们假设显式建模生物上稳健的核形态及空间组织将能够学习到在领域变化中具有鲁棒性的癌症表示。为此，我们提出了MorphGen（基于形态特征的泛化方法），这是一种结合了病理图像、数据增强和细胞核分割掩膜，在监督对比学习框架内的方法。通过使图像与核掩模之间的潜在表征对齐，MorphGen优先考虑诊断性特征（如核异常及形态学异常以及空间组织）而非染色伪影和特定领域的特性。 为了进一步提高域外数据的稳健性，我们引入了随机权重平均化（SWA），这种方法导向寻找更平坦的极小值。注意力图分析显示，MorphGen主要依赖于肿瘤或正常区域内的核形态、细胞组成以及空间细胞组织来进行最终分类。 最后，我们展示了所学表示在面对图像损坏（如染色伪影）和对抗性攻击时的鲁棒性，不仅表现出域外泛化能力，还解决了当前数字病理学深度学习系统中的关键漏洞问题。相关代码、数据集及训练模型可从以下网址获取：[提供链接]
URL
https://arxiv.org/abs/2509.00311
