16 papers
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…
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…
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…
Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models
Runchen Wang, Junlin Guo, Siqi Lu +12
Accurate cell nuclei segmentation is critical for downstream tasks in kidney pathology and remains a major challenge due to the morphological diversity and imaging variability of r…