ITC-RWKV: Interactive Tissue-Cell Modeling with Recurrent Key-Value Aggregation for Histopathological Subtyping

TL;DR

This paper introduces a dual-stream recurrent transformer model that effectively integrates tissue-level and cell-level features for improved histopathological subtyping, addressing limitations of existing global models.

Contribution

It proposes a novel tissue-cell interaction architecture with linear complexity aggregation, enhancing fine-grained pathology classification accuracy.

Findings

Outperforms existing models on four classification benchmarks.

Highlights the importance of cell-level features in tissue context.

Demonstrates efficient inter-cell dependency modeling.

Abstract

Accurate interpretation of histopathological images demands integration of information across spatial and semantic scales, from nuclear morphology and cellular textures to global tissue organization and disease-specific patterns. Although recent foundation models in pathology have shown strong capabilities in capturing global tissue context, their omission of cell-level feature modeling remains a key limitation for fine-grained tasks such as cancer subtype classification. To address this, we propose a dual-stream architecture that models the interplay between macroscale tissue features and aggregated cellular representations. To efficiently aggregate information from large cell sets, we propose a receptance-weighted key-value aggregation model, a recurrent transformer that captures inter-cell dependencies with linear complexity. Furthermore, we introduce a bidirectional tissue-cell interaction module to enable mutual attention between localized cellular cues and their surrounding tissue environment. Experiments on four histopathological subtype classification benchmarks show that the proposed method outperforms existing models, demonstrating the critical role of cell-level aggregation and tissue-cell interaction in fine-grained computational pathology.