PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability.

Building on the success of the Segment Anything Model (SAM) in image segmentation, "PathSAM: SAM for Pathological Images in Oral Cancer Detection" addresses the unique challenges associated with diagnosing oral cancer. Although SAM is versatile, its application to pathological images is hindered by its inherent complexity and variability. PathSAM advances beyond traditional deep-learning methods by delivering superior accuracy and detail in segmenting critical datasets like ORCA and OCDC, as demonstrated through both quantitative and qualitative evaluations. The integration of Large Language Models (LLMs) further enhances PathSAM by providing clear, interpretable segmentation results, facilitating accurate tumor identification, and improving communication between patients and healthcare providers. This innovation positions PathSAM as a valuable tool in medical diagnostics.