arXiv:2510.21479v1 Announce Type: new 
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.

تُعدّ المقدمة الحديثة لـ ITC-RWKV تقدمًا كبيرًا في مجال علم الأمراض النسيجية من خلال تعزيز نمذجة تفاعلات الأنسجة والخلايا. تدمج هذه الطريقة المبتكرة المعلومات المكانية والدلالية، مما يسمح بتفسيرات أكثر دقة للصور النسيجية. من خلال معالجة القيود التي كانت تعاني منها النماذج السابقة التي تجاهلت الميزات على مستوى الخلية، يعد ITC-RWKV بتحسين دقة تشخيص السرطان وتخطيط العلاج، مما يجعله تطورًا حاسمًا للمهنيين الطبيين والباحثين على حد سواء.

La reciente introducción de ITC-RWKV marca un avance significativo en el campo de la histopatología al mejorar la modelización de las interacciones tejido-célula. Este enfoque innovador integra información espacial y semántica, permitiendo interpretaciones más precisas de las imágenes histopatológicas. Al abordar las limitaciones de modelos anteriores que pasaban por alto las características a nivel celular, ITC-RWKV promete mejorar la precisión de los diagnósticos y la planificación del tratamiento del cáncer, convirtiéndose en un desarrollo crucial para los profesionales médicos e investigadores.

La récente introduction de l'ITC-RWKV marque une avancée significative dans le domaine de l'histopathologie en améliorant la modélisation des interactions tissu-cellule. Cette approche innovante intègre à la fois des informations spatiales et sémantiques, permettant des interprétations plus précises des images histopathologiques. En répondant aux limites des modèles précédents qui négligeaient les caractéristiques au niveau cellulaire, l'ITC-RWKV promet d'améliorer la précision des diagnostics et de la planification des traitements du cancer, ce qui en fait un développement crucial pour les professionnels de la santé et les chercheurs.

The recent introduction of ITC-RWKV marks a significant advancement in the field of histopathology by enhancing the modeling of tissue-cell interactions. This innovative approach integrates both spatial and semantic information, allowing for more accurate interpretations of histopathological images. By addressing the limitations of previous models that overlooked cell-level features, ITC-RWKV promises to improve the precision of cancer diagnostics and treatment planning, making it a crucial development for medical professionals and researchers alike.

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

arXiv:2601.08127v1 Announce Type: new 
Abstract: The development of robust artificial intelligence models for histopathology diagnosis is severely constrained by the scarcity of expert-annotated lesion data, particularly for rare pathologies and underrepresented disease subtypes. While data augmentation offers a potential solution, existing methods fail to generate sufficiently realistic lesion morphologies that preserve the complex spatial relationships and cellular architectures characteristic of histopathological tissues. Here we present PathoGen, a diffusion-based generative model that enables controllable, high-fidelity inpainting of lesions into benign histopathology images. Unlike conventional augmentation techniques, PathoGen leverages the iterative refinement process of diffusion models to synthesize lesions with natural tissue boundaries, preserved cellular structures, and authentic staining characteristics. We validate PathoGen across four diverse datasets representing distinct diagnostic challenges: kidney, skin, breast, and prostate pathology. Quantitative assessment confirms that PathoGen outperforms state-of-the-art generative baselines, including conditional GAN and Stable Diffusion, in image fidelity and distributional similarity. Crucially, we show that augmenting training sets with PathoGen-synthesized lesions enhances downstream segmentation performance compared to traditional geometric augmentations, particularly in data-scarce regimes. Besides, by simultaneously generating realistic morphology and pixel-level ground truth, PathoGen effectively overcomes the manual annotation bottleneck. This approach offers a scalable pathway for developing generalizable medical AI systems despite limited expert-labeled data.

يمثل تقديم PathoGen، وهو نموذج توليدي يعتمد على الانتشار، تقدمًا كبيرًا في تركيب الآفات الواقعية في صور علم الأمراض النسيجية، حيث يعالج النقص الحاد في بيانات الآفات المعلّمة من قبل الخبراء، خاصةً بالنسبة للأمراض النادرة. يعزز هذا النموذج عملية إدخال الآفات في الصور الحميدة مع الحفاظ على الحدود الطبيعية للأنسجة والهياكل الخلوية.

La introducción de PathoGen, un modelo generativo basado en difusión, marca un avance significativo en la síntesis de lesiones realistas en imágenes de histopatología, abordando la escasez crítica de datos de lesiones anotados por expertos, especialmente para patologías raras. Este modelo mejora la inpainting de lesiones en imágenes benignas mientras preserva los límites naturales de los tejidos y las estructuras celulares.

L'introduction de PathoGen, un modèle génératif basé sur la diffusion, représente une avancée significative dans la synthèse de lésions réalistes dans les images d'histopathologie, répondant à la pénurie critique de données de lésions annotées par des experts, en particulier pour les pathologies rares. Ce modèle améliore l'inpainting de lésions dans des images bénignes tout en préservant les frontières naturelles des tissus et les structures cellulaires.

The introduction of PathoGen, a diffusion-based generative model, marks a significant advancement in the synthesis of realistic lesions in histopathology images, addressing the critical shortage of expert-annotated lesion data, especially for rare pathologies. This model enhances the inpainting of lesions into benign images while preserving natural tissue boundaries and cellular structures.

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

Was this article worth reading? Share it

LucidQuery AI

File Architect

VECTARY

AIPortalX

Resub

Tattoo Visualizer

Ready to build your own newsroom?