Domain generalised cell nuclei segmentation in histopathology images using domain-aware curriculum learning and colour-perceived meta learning

Cell nuclei segmentation in histopathology images is critical in computer-aided diagnosis and treatment planning. However, this task is challenging due to inherent heterogeneity in histopathology images especially when originating from different domains, caused by variations in imaging protocols, staining techniques, and tissue preparation methods. Such domain shifts can significantly affect segmentation performance when the segmentation model is trained and tested on different domains. In this work, we present a novel gradient-based meta-learning approach for domain generalisation in histopathology cell nuclei segmentation. Specifically, we propose a domain-aware regularisation to correct each pixel’s classification based on the specific domain. We also embed a novel network module to preserve the colour features in histopathology images via an enhanced feature extraction procedure. We demonstrate that our proposed framework can achieve consistent and accurate segmentation performance across domains through extensive experiments on multiple histopathology datasets from diverse sources. Our code is available at: unmapped: uri https://github.com/winnie172026/DG.