聚焦细胞核形态提升病理图像跨医院泛化能力
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
- 用原始图像与核分割掩码联合训练，引导模型关注细胞核
- 在多个数据集上显著提升跨医院泛化性能
- 对图像噪声和对抗攻击更鲁棒，适合医疗影像部署
计算病理学中的领域泛化挑战主要源于医院间组织固定、染色及成像设备差异带来的图像变化。我们假设聚焦于细胞核可改善癌症检测的域外泛化能力。提出一种简单方法：在训练中融合原始图像与核分割掩码，促使模型关注细胞核形态及其空间排列——这些在癌症检测中具有域不变性的关键特征。不同于传统数据增强，我们引入一种正则化技术，使掩码与原始图像的特征表示对齐。实验结果表明，该方法在多个数据集上均提升了域外泛化能力，并增强了对图像退化与对抗攻击的鲁棒性。代码已开源：https://github.com/undercutspiky/SFL
原文摘要 · Abstract (English)
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/
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