Unsupervised Domain Adaptation for Multi-Stain Cell Detection in Breast Cancer with Transformers

The complexity of digital pathology image analysis arises from histopathological slide variability, including tissue specimen differences and stain variations. While publicly available datasets primarily focus on hematoxylin and eosin (H&E) staining, pathologists often require analysis across multiple stains for comprehensive diagnosis. Deep learning pipelines’ implementation in clinical settings is hindered by poor cross-stain generalization, necessitating exhaustive annotations for each stain, which are time-consuming to obtain. In this work, we address these challenges by focusing on breast cancer analysis across four crucial stains: ER, PR, HER2, and Ki-67. Given the necessity of cell-level information for diagnosis, we concentrate on cell detection tasks with detection transformers. Leveraging unsupervised domain adaptation techniques, we bridge the gap between publicly available, annotated H&E datasets and unlabeled data in other stains. We demonstrate the superiority of adversarial feature learning over source-only and image-level generative methods. Our work contributes to improving digital pathology image analysis by enabling robust and efficient computer-aided diagnosis pipelines across multiple stains, thereby improving diagnostic accuracy in practical settings. The code can be found at https://github.com/oscar97pina/stain-celldetr.