Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Camelyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification.

翻译：随着深度学习的兴起，数字病理学在基于组织学图像的癌症诊断中日益普及。深度弱监督目标定位（WSOL）模型可利用廉价的全局图像类别标注，根据癌症等级对组织学图像进行分类，并识别感兴趣区域（ROI）用于解释。当染色、扫描仪和癌症类型差异导致显著域偏移时，最初在部分标注源图像数据上训练的WSOL模型可通过无标注目标数据进行适应。本文聚焦于无源（无监督）域适应（SFDA）这一具有挑战性的问题：基于隐私和效率考虑，需在完全不使用源域数据的情况下，将预训练源模型适应至新目标域。WSOL模型的SFDA在组织学中面临多重挑战，尤其是此类模型需同时适应分类与定位任务。本文比较了4种代表性SFDA方法（每种均为主要SFDA家族的代表）在WSOL分类与定位精度上的表现，包括：基于分布估计的SFDA、源假设迁移、跨域对比学习、自适应域统计对齐。在具有挑战性的Glas（小型，乳腺癌）和Camelyon16（大型，结肠癌）组织学数据集上的实验结果表明：当以分类任务为优化目标时，这些SFDA方法在适应后的定位精度普遍较低。