Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Alexis Guichemerre, Soufiane Belharbi, Tsiry Mayet, Shakeeb Murtaza, Pourya Shamsolmoali, Luke Mccaffrey, Eric Granger; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 33-43

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 to 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.