Cluster Triplet Loss for Unsupervised Domain Adaptation on Histology Images

Ruby Wood, Enric Domingo, Viktor Hendrik Koelzer, Timothy S. Maughan, Jens Rittscher; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5122-5131

Deep learning models that predict cancer patient treatment response from medical images need to be generalisable across different patient cohorts. However this can be difficult due to heterogeneity across patient populations. Here we focus on the problem of predicting colorectal cancer patients' response to radiotherapy from histology images scanned from tumour biopsies and adapt this prediction model onto a new visibly different target cohort of patients. We present a novel unsupervised domain adaptation method with a Cluster Triplet Loss function using minimal information from the source domain resulting in an improvement in AUC from 0.544 to 0.818 on the target cohort. We avoid the use of pseudo-labels and class feature centres to avoid adding noise and bias to the adapted model and perform experiments to verify the preferable performance of our model over such state-of-the-art methods. Our proposed approach can be applied in many complex medical imaging cases including prediction on large whole slide images based on combining predictions from smaller memory-feasible representations of the image extracted from graph neural networks.