Abstract: 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.
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