PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability
Presentation Time: 09:30 AM - 09:45 AM
Abstract Keywords: Machine Learning, Cancer Prevention, Diagnostic Systems, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Leveraging the Segment Anything Model's (SAM) success in general image segmentation, "PathSAM: SAM for Pathological Images for Oral Cancer Detection" specifically targets the nuanced challenges of oral cancer diagnosis. Although SAM is celebrated for its adaptability, its application to pathological images is hindered by their complexity and variability. PathSAM outperforms conventional deep-learning approaches, demonstrating superior accuracy and detail on critical datasets like ORCA and OCDC through both quantitative and qualitative measures. By integrating Large Language Models (LLMs), PathSAM significantly enhances the explainability of its segmentation results, a vital feature for accurately identifying tumors and improving patient-provider communication. This capability to navigate the intricacies of pathological images cements PathSAM's role as an innovative solution in the field of medical diagnostics.
Speaker(s):
Suraj Sood, Ph.D
University of Missouri-Kansas City
Presentation Time: 09:30 AM - 09:45 AM
Abstract Keywords: Machine Learning, Cancer Prevention, Diagnostic Systems, Large Language Models (LLMs)
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF
Leveraging the Segment Anything Model's (SAM) success in general image segmentation, "PathSAM: SAM for Pathological Images for Oral Cancer Detection" specifically targets the nuanced challenges of oral cancer diagnosis. Although SAM is celebrated for its adaptability, its application to pathological images is hindered by their complexity and variability. PathSAM outperforms conventional deep-learning approaches, demonstrating superior accuracy and detail on critical datasets like ORCA and OCDC through both quantitative and qualitative measures. By integrating Large Language Models (LLMs), PathSAM significantly enhances the explainability of its segmentation results, a vital feature for accurately identifying tumors and improving patient-provider communication. This capability to navigate the intricacies of pathological images cements PathSAM's role as an innovative solution in the field of medical diagnostics.
Speaker(s):
Suraj Sood, Ph.D
University of Missouri-Kansas City
PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability
Category
Paper - Student