Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology

TL;DR

This paper investigates the effects of various SAM ablations and fine-tuning on medical image segmentation accuracy in radiology and pathology, highlighting its potential to improve healthcare diagnostics.

Contribution

It provides a detailed analysis of SAM's components, interactions, and fine-tuning impacts specifically for medical imaging applications in radiology and pathology.

Findings

SAM fine-tuning improves segmentation accuracy

Ablation studies reveal key components influencing performance

Enhanced reliability in brain tumor and breast cancer segmentation

Abstract

Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation. The Segment Anything Model (SAM) has emerged as a promising framework for addressing segmentation challenges across different domains. In this white paper, we delve into SAM, breaking down its fundamental components and uncovering the intricate interactions between them. We also explore the fine-tuning of SAM and assess its profound impact on the accuracy and reliability of segmentation results, focusing on applications in radiology (specifically, brain tumor segmentation) and pathology (specifically, breast cancer segmentation). Through a series of carefully designed experiments, we analyze SAM's potential application in the field of medical imaging. We aim to bridge the gap between advanced segmentation techniques and the demanding requirements of healthcare, shedding light on SAM's transformative capabilities.