FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis

Medical image segmentation is a critical process in the field of medical imaging, playing a pivotal role in diagnosis, treatment, and research. It involves partitioning of an image into multiple regions, representing distinct anatomical or pathological structures. Conventional methods often grapple with the challenge of balancing spatial precision and comprehensive feature representation due to their reliance on traditional loss functions. To overcome this, we propose Feature-Enhanced Spatial Segmentation Loss (FESS Loss), that integrates the benefits of contrastive learning (which extracts …

analysis challenge cs.ai cs.cv diagnosis feature image imaging loss medical medical imaging multiple partitioning pivotal playing precision process research role segmentation spatial treatment