A Dual-branch Network with Cross-scale Feature Interaction and Alignment for Weakly Supervised Whole Slide Image Analysis.

Abstract

Whole slide images (WSIs) analysis plays a critical role in computer-aided diagnosis. Recently, weakly supervised multiple instance learning (MIL) has become a widely adopted approach for WSI processing, as it enables effective learning from slide-level labels without the need for exhaustive pixel-level annotations. However, many existing MIL methods do not fully leverage the inherent pyramidal structure of WSIs and struggle to capture dependencies among instances and local contextual information, which may restrict their ability to capture rich hierarchical information. To address these issues, this paper proposes FIA-MIL, a dual-branch network designed for weakly supervised WSIs analysis that incorporates cross-scale feature interaction and alignment. Specifically, a dual-scale feature interaction module models semantic relationships across magnifications using a pyramid-aligned two-branch architecture, with Transformer encoders capturing instance-level dependencies within each scale. Furthermore, a dual-scale feature aggregation module integrates multi-scale features and introduces alignment constraints at both the bag and instance levels to ensure semantic consistency across scales. Classification and survival analysis experiments conducted on publicly available WSIs datasets demonstrate that the proposed method exhibits promising performance.