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20242026
most citedASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics

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cs.CV2025

HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes

Yuechen Yang, Junlin Guo, Yanfan Zhu +10

High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevan…

cs.CV2025

From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology

Zhenhao Guo, Rachit Saluja, Tianyuan Yao +13

Fine-grained glomerular subtyping is central to kidney biopsy interpretation, but clinically valuable labels are scarce and difficult to obtain. Existing computational pathology ap…

cs.LG2025

Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework

Chongyu Qu, Allen J. Luna, Thomas Z. Li +6

Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all co…

cs.CV2025

Img2ST-Net: Efficient High-Resolution Spatial Omics Prediction from Whole Slide Histology Images via Fully Convolutional Image-to-Image Learning

Junchao Zhu, Ruining Deng, Junlin Guo +10

Recent advances in multi-modal AI have demonstrated promising potential for generating the currently expensive spatial transcriptomics (ST) data directly from routine histology ima…

cs.CV2025

ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction

Juming Xiong, Ruining Deng, Jialin Yue +10

Histological analysis plays a crucial role in understanding tissue structure and pathology. While recent advancements in registration methods have improved 2D histological analysis…

eess.IV2025

IRS: Incremental Relationship-guided Segmentation for Digital Pathology

Ruining Deng, Junchao Zhu, Juming Xiong +14

Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practica…