1 citations · 5 across the 18 of their papers we have counts for
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Comprehensive Evaluation and Fine-Tuning of Foundational Cell Nuclei Segmentation Models in Renal Pathology
Ruijie Wu, Junlin Guo, Ruining Deng +4
Accurate nuclei instance segmentation is essential for quantitative renal pathology, yet general-purpose models often struggle with low contrast, dense nuclei, complex morphology,…
Explainable Pathomics Feature Visualization via Correlation-aware Conditional Feature Editing
Yuechen Yang, Junlin Guo, Ruining Deng +9
Pathomics is a recent approach that offers rich quantitative features beyond what black-box deep learning can provide, supporting more reproducible and explainable biomarkers in di…
SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
Junchao Zhu, Ruining Deng, Junlin Guo +13
Spatial transcriptomics (ST) is an emerging technology that enables researchers to investigate the molecular relationships underlying tissue morphology. However, acquiring ST data…
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…
How Close Are We? Limitations and Progress of AI Models in Banff Lesion Scoring
Yanfan Zhu, Juming Xiong, Ruining Deng +7
The Banff Classification provides the global standard for evaluating renal transplant biopsies, yet its semi-quantitative nature, complex criteria, and inter-observer variability p…
MedFoundationHub: A Lightweight and Secure Toolkit for Deploying Medical Vision Language Foundation Models
Xiao Li, Yanfan Zhu, Ruining Deng +6
Recent advances in medical vision-language models (VLMs) open up remarkable opportunities for clinical applications such as automated report generation, copilots for physicians, an…
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…
MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics
Junchao Zhu, Ruining Deng, Tianyuan Yao +12
The rapid development of spatial transcriptomics (ST) offers new opportunities to explore the gene expression patterns within the spatial microenvironment. Current research integra…
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Ruining Deng, Tianyuan Yao, Yucheng Tang +44
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard f…
ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics
Junchao Zhu, Ruining Deng, Tianyuan Yao +9
Spatial transcriptomics (ST) is an emerging technology that enables medical computer vision scientists to automatically interpret the molecular profiles underlying morphological fe…
Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment
Junlin Guo, Siqi Lu, Can Cui +14
Training AI foundation models has emerged as a promising large-scale learning approach for addressing real-world healthcare challenges, including digital pathology. While many of t…