Deep Learning, Artificial Intelligence, Medical Imaging, Image Segmentation, Transformers.
validation results, the proposed model does not suffer from over-fitting. Comparison of segmentation performance on the 13 testing subjects of iSeg-2017. In the iSeg-2017 challenge, according to the leaderboard, the proposed model achieves one of the top performances. 3.2. Model & Time Complexity