Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology
  and Pathology
Amin Ranem Niklas Babendererde Moritz Fuchs Anirban Mukhopadhyay
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.