Semantic segmentations of pathological entities have crucial clinical value
in computational pathology workflows. Foundation models, such as the Segment
Anything Model (SAM), have been recently proposed for universal use in
segmentation tasks. SAM shows remarkable promise in instance segmentation on
natural images. However, the applicability of SAM to computational pathology
tasks is limited due to the following factors: (1) lack of comprehensive
pathology datasets used in SAM training and (2) the design of SAM is not
inherently optimized for semantic segmentation tasks. In this work, we adapt
SAM for semantic segmentation by introducing trainable class prompts, followed
by further enhancements through the incorporation of a pathology encoder,
specifically a pathology foundation model. Our framework, SAM-Path enhances
SAM's ability to conduct semantic segmentation in digital pathology without
human input prompts. Through experiments on two public pathology datasets, the
BCSS and the CRAG datasets, we demonstrate that the fine-tuning with trainable
class prompts outperforms vanilla SAM with manual prompts and post-processing
by 27.52% in Dice score and 71.63% in IOU. On these two datasets, the proposed
additional pathology foundation model further achieves a relative improvement
of 5.07% to 5.12% in Dice score and 4.50% to 8.48% in IOU.

通过引入可训练的类别提示和病理编码器，本研究将 Segment Anything Model（SAM）适应于数字病理学中的语义分割任务，进一步通过增加病理基础模型提高了 SAM-Path 在数字病理学中的语义分割能力，实验证明在两个公共病理学数据集上，可训练类别提示的微调方案在 Dice 得分上超过手动提示和后处理的 SAM27.52％，在 IOU 得分上超过 71.63％，而增加病理基础模型相对提升 Dice 得分 5.07％至 5.12％，IOU 得分 4.50％至 8.48％。