Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation
This addresses domain shift issues in medical image analysis for histopathology, though it appears incremental as it builds on existing U-Net architectures.
The paper tackled cross-domain adenocarcinoma segmentation in histopathology by developing a U-Net-based framework, achieving scores of 0.8020 for cross-organ and 0.8527 for cross-scanner tracks, ranking as the best submission in the COSAS challenge.
Recent advances in computer-aided diagnosis for histopathology have been largely driven by the use of deep learning models for automated image analysis. While these networks can perform on par with medical experts, their performance can be impeded by out-of-distribution data. The Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS) challenge aimed to address the task of cross-domain adenocarcinoma segmentation in the presence of morphological and scanner-induced domain shifts. In this paper, we present a U-Net-based segmentation framework designed to tackle this challenge. Our approach achieved segmentation scores of 0.8020 for the cross-organ track and 0.8527 for the cross-scanner track on the final challenge test sets, ranking it the best-performing submission.