activity
20202026
most citedSegment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

97 citations · 211 across the 77 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

9 papers · 2 filters

cs.CV2024★ 1 cited

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…

cs.CV2024

GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation

Jialin Yue, Tianyuan Yao, Ruining Deng +7

Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) i…

cs.CV2024★ 1 cited

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…

cs.CV2024

Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching

Muyang Li, Juming Xiong, Ruining Deng +4

Endoscopy is a crucial tool for diagnosing the gastrointestinal tract, but its effectiveness is often limited by a narrow field of view and the dynamic nature of the internal envir…

cs.CV2024★ 1 cited

PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models

Can Cui, Ruining Deng, Junlin Guo +4

The Vision Foundation Model has recently gained attention in medical image analysis. Its zero-shot learning capabilities accelerate AI deployment and enhance the generalizability o…

cs.CV2024

Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results

Jialin Yue, Tianyuan Yao, Ruining Deng +5

Recently, the use of circle representation has emerged as a method to improve the identification of spherical objects (such as glomeruli, cells, and nuclei) in medical imaging stud…