ITC-RWKV: Interactive Tissue-Cell Modeling with Recurrent Key-Value Aggregation for Histopathological Subtyping
- URL: http://arxiv.org/abs/2510.21479v1
- Date: Fri, 24 Oct 2025 14:03:52 GMT
- Title: ITC-RWKV: Interactive Tissue-Cell Modeling with Recurrent Key-Value Aggregation for Histopathological Subtyping
- Abstract summary: We propose a dual-stream architecture that models the interplay between macroscale tissue features and aggregated cellular representations.<n>We introduce a bidirectional tissue-cell interaction module to enable mutual attention between localized cellular cues and their surrounding tissue environment.
- Score: 2.99938892718088
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells [48.885716676667776]
Lingshu-Cell is a discrete diffusion model that learns transcriptomic state distributions and supports conditional simulation under perturbations.<n>It captures complex transcriptome-wide expression dependencies across approximately 18,000 genes without relying on prior gene selection.<n>It achieves leading performance on the Virtual Cell Challenge H1 genetic perturbation benchmark and in predicting cytokine-induced responses in human PBMCs.
arXiv Detail & Related papers (2026-03-26T09:46:27Z) - Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling [74.25438319700929]
We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that models local-global dependencies between molecules and cellular responses.<n> evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines.<n>Results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations.
arXiv Detail & Related papers (2025-11-26T07:15:00Z) - Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow [80.00833033784079]
Understanding of cellular microenvironment is essential for deciphering tissue development and disease data.<n>NicheFlow is a flow-based generative model that infers the temporal trajectory of cellular microenvironments across spatial slides.
arXiv Detail & Related papers (2025-11-02T15:41:38Z) - CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis [51.56484100374058]
We introduce CellPainTR, a Transformer-based architecture designed to learn foundational representations of cellular morphology.<n>Our work represents a significant step towards creating truly foundational models for image-based profiling, enabling more reliable and scalable cross-study biological analysis.
arXiv Detail & Related papers (2025-09-02T03:30:07Z) - MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [57.044719143401664]
Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
arXiv Detail & Related papers (2025-03-01T07:02:30Z) - HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification [0.19791587637442667]
This study introduces a novel single-stage approach for generating image-label pairs to augment histology datasets.<n>Unlike state-of-the-art methods that utilize diffusion models with separate components for label and image generation, our approach employs a latent diffusion model.<n>This model enables tailored data generation by conditioning on user-defined parameters such as cell types, quantities, and tissue types.
arXiv Detail & Related papers (2025-02-12T19:51:41Z) - TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model [32.670806339139034]
We propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies.<n>Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks.
arXiv Detail & Related papers (2024-12-08T18:02:22Z) - Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition.
We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
arXiv Detail & Related papers (2024-07-14T04:41:16Z) - UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
We propose a universal cell nucleus classification framework (UniCell)
It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains.
In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
arXiv Detail & Related papers (2024-02-20T11:50:27Z) - Single-Cell Deep Clustering Method Assisted by Exogenous Gene
Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells.
During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation.
This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
arXiv Detail & Related papers (2023-11-28T09:14:55Z) - Topology-Guided Multi-Class Cell Context Generation for Digital
Pathology [28.43244574309888]
We introduce several mathematical tools from spatial statistics and topological data analysis.
We generate high quality multi-class cell layouts for the first time.
We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
arXiv Detail & Related papers (2023-04-05T07:01:34Z) - OCELOT: Overlapped Cell on Tissue Dataset for Histopathology [13.691924123273004]
We release OCELOT, a dataset dedicated to the study of cell-tissue relationships for cell detection in histopathology.
We propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously.
On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score.
arXiv Detail & Related papers (2023-03-23T08:57:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.