FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
This work addresses a critical problem for medical imaging practitioners by improving segmentation accuracy, though it appears incremental as it combines existing methods.
The paper tackles the challenge of balancing spatial precision and feature representation in medical image segmentation by proposing FESS Loss, which integrates contrastive learning with Dice loss, resulting in more accurate segmentation, particularly in scenarios with limited annotated data.
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 intricate features, particularly in the nuanced domain of medical imaging) with the spatial accuracy inherent in the Dice loss. The objective is to augment both spatial precision and feature-based representation in the segmentation of medical images. FESS Loss signifies a notable advancement, offering a more accurate and refined segmentation process, ultimately contributing to heightened precision in the analysis of medical images. Further, FESS loss demonstrates superior performance in limited annotated data availability scenarios often present in the medical domain.