PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
Current methods for pathology image segmentation typically treat 2D slices
independently, ignoring valuable cross-slice information. We present
PathSeqSAM, a novel approach that treats 2D pathology slices as sequential
video frames using SAM2's memory mechanisms. Our method introduces a
distance-aware attention mechanism that accounts for variable physical
distances between slices and employs LoRA for domain adaptation. Evaluated on
the KPI Challenge 2024 dataset for glomeruli segmentation, PathSeqSAM
demonstrates improved segmentation quality, particularly in challenging cases
that benefit from cross-slice context. We have publicly released our code at
https://github.com/JackyyyWang/PathSeqSAM.
