Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation

• URL: http://arxiv.org/abs/2409.09797v1
• Date: Sun, 15 Sep 2024 17:08:34 GMT
• Title: Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation
• Authors: Frauke Wilm, Mathias Öttl, Marc Aubreville, Katharina Breininger,
• Abstract summary: We present a U-Net-based segmentation framework to tackle the Cross-Organ and Cross-Scanner Adenocarcinoma (COSAS) 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.
• Score: 0.44569778071604066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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