标题： MorphGen：用于组织病理学癌症分类中鲁棒单域泛化的形态学引导表示学习 显示英文标题

标题： MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification

摘要： 计算组织病理学中的领域泛化受到全幻灯片图像（WSIs）的异质性阻碍，这是由于不同机构在组织制备、染色和成像条件方面的变化所致。 与机器学习系统不同，病理学家依赖于领域不变的形态学线索，例如核异型性（增大、不规则轮廓、高染色性、染色质纹理、空间紊乱）、结构异型性（异常的结构和腺体形成）以及总体形态学异型性，在各种环境中仍具有诊断价值。 受此启发，我们假设显式建模生物上稳健的核形态和空间组织将有助于学习对领域转移具有鲁棒性的癌症表示。 我们提出了MorphGen（形态学引导的泛化），一种在监督对比学习框架中整合组织病理学图像、增强图像和核分割掩码的方法。 通过对齐图像和核掩码的潜在表示，MorphGen优先考虑如核异型性、形态学异型性和空间组织等诊断特征，而不是染色伪影和领域特定特征。 为了进一步提高分布外的鲁棒性，我们引入了随机权重平均（SWA），引导优化朝向更平坦的最小值。 注意力图分析显示，MorphGen主要依赖肿瘤或正常区域内的核形态、细胞组成和空间细胞组织进行最终分类。 最后，我们展示了所学表示对图像损坏（如染色伪影）和对抗攻击的鲁棒性，不仅展示了OOD泛化，还解决了当前数字病理学深度学习系统的关键漏洞。 代码、数据集和训练好的模型可在以下网址获取：https://github.com/hikmatkhan/MorphGen 显示英文摘要

摘要： 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: https://github.com/hikmatkhan/MorphGen