SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage
  Segmentation
Yinuo Wang Kai Chen Weimin Yuan Cai Meng XiangZhi Bai
Segment Anything Model (SAM), a vision foundation model trained on
large-scale annotations, has recently continued raising awareness within
medical image segmentation. Despite the impressive capabilities of SAM on
natural scenes, it struggles with performance decline when confronted with
medical images, especially those involving blurry boundaries and highly
irregular regions of low contrast. In this paper, a SAM-based
parameter-efficient fine-tuning method, called SAMIHS, is proposed for
intracranial hemorrhage segmentation, which is a crucial and challenging step
in stroke diagnosis and surgical planning. Distinguished from previous SAM and
SAM-based methods, SAMIHS incorporates parameter-refactoring adapters into
SAM's image encoder and considers the efficient and flexible utilization of
adapters' parameters. Additionally, we employ a combo loss that combines binary
cross-entropy loss and boundary-sensitive loss to enhance SAMIHS's ability to
recognize the boundary regions. Our experimental results on two public datasets
demonstrate the effectiveness of our proposed method. Code is available at
https://github.com/mileswyn/SAMIHS .