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/
翻译:计算组织病理学中的领域泛化面临严峻挑战,主要源于不同医院间因组织固定、染色及成像设备等因素导致的图像显著差异。我们假设聚焦于细胞核特征能够提升癌症检测的跨域泛化性能。为此,我们提出一种通过关注细胞核形态与空间排布来改进癌症检测跨域泛化的简洁方法,因为这些特征作为领域不变属性在癌症诊断中至关重要。该方法在训练过程中将原始图像与细胞核分割掩模相融合,促使模型优先学习细胞核及其空间分布特征。我们进一步超越单纯的数据增强策略,引入一种正则化技术以实现掩模与原始图像表征的对齐。通过在多个数据集上的实验验证,本方法不仅提升了跨域泛化能力,还增强了模型对图像损坏与对抗攻击的鲁棒性。源代码已公开于 https://github.com/undercutspiky/SFL/。