Adenocarcinoma Segmentation Using Pre-trained Swin-UNet with Parallel Cross-Attention for Multi-Domain Imaging
<|reference_start|>Adenocarcinoma Segmentation Using Pre-trained Swin-UNet with Parallel Cross-Attention for Multi-Domain Imaging: Computer aided pathological analysis has been the gold standard for tumor diagnosis, however domain shift is a significant problem in histopathology. It may be caused by variability in anatomical structures, tissue preparation, and imaging processes challenges the robustness of segmentation models. In this work, we present a framework consist of pre-trained encoder with a Swin-UNet architecture enhanced by a parallel cross-attention module to tackle the problem of adenocarcinoma segmentation across different organs and scanners, considering both morphological changes and scanner-induced domain variations. Experiment conducted on Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation challenge dataset showed that our framework achieved segmentation scores of 0.7469 for the cross-organ track and 0.7597 for the cross-scanner track on the final challenge test sets, and effectively navigates diverse imaging conditions and improves segmentation accuracy across varying domains.<|reference_end|>
@article{qayyum2024adenocarcinoma,
    title={Adenocarcinoma Segmentation Using Pre-trained Swin-UNet with Parallel
  Cross-Attention for Multi-Domain Imaging},
    author={Abdul Qayyum, Moona Mazher Imran Razzak, and Steven A Niederer},
    journal={arXiv preprint arXiv:2409.15501},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15501},
    primaryClass={eess.IV cs.CV}
}