Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification

arXiv:2403.18134v1 Announce Type: cross
Abstract: In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pixel WSIs are only labeled at the slide level. However, existing attention-based MIL approaches often overlook contextual information and intrinsic spatial relationships between neighboring tissue tiles, while graph-based MIL frameworks have limited power to recognize the long-range dependencies. In this paper, we introduce the integrative graph-transformer framework that simultaneously captures the …

abstract arxiv attention classification cs.cv digital digital pathology eess.iv framework graph however image information instance intrinsic mil multiple pathology pixel representation strategy transformer type