核掩码是提升域泛化能力的全部所需吗？深入探究组织病理学中的癌症分类
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
AI总结：
针对计算组织病理学中因医院间差异导致的域泛化难题，提出结合核分割掩码与表征对齐正则化的方法，聚焦核形态等域不变特征，有效提升癌症检测的域外泛化性及抗损坏、抗攻击鲁棒性。
摘要：
计算组织病理学中的域泛化极具挑战性，因为组织固定、染色方式及成像设备等因素导致的医院间差异会对图像产生显著影响。我们假设聚焦细胞核能够提升癌症检测中的域外（OOD）泛化能力。我们提出一种提升癌症检测域外泛化能力的简单方法，该方法聚焦于细胞核形态与组织结构——这些是癌症检测中关键的域不变特征。我们的方法在训练过程中将原始图像与核分割掩码相结合，促使模型优先关注细胞核及其空间排布。我们并未止步于单纯的数据增强，还引入了一种正则化技术，用于对齐掩码与原始图像的表征。我们通过多个数据集证明，该方法不仅提升了域外泛化能力，还增强了模型对图像损坏和对抗攻击的鲁棒性。源代码可在https://github.com/undercutspiky/SFL/ 获取。
英文摘要：
Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation, as these are domain-invariant features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks. The source code is available at https://github.com/undercutspiky/SFL/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
