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cs.CV2024
EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything
Joonhyeon Song, Seohwan Yun, Seongho Yoon +2
This work proposes a novel approach beyond supervised learning for effective pathological image analysis, addressing the challenge of limited robust labeled data. Pathological diag…
cs.CV2024
CAD: Memory Efficient Convolutional Adapter for Segment Anything
Joohyeok Kim, Joonhyeon Song, Seohwan Yun +2
The Foundation model for image segmentation, Segment Anything (SAM), has been actively researched in various fields since its proposal. Various researches have been proposed to ada…