15 citations · 28 across the 6 of their papers we have counts for
6 papers
MedVision: Benchmarking Quantitative Medical Image Analysis
Yongcheng Yao, Yongshuo Zong, Raman Dutt +3
Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks.…
CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs
Raman Dutt, Pedro Sanchez, Yongchen Yao +3
Structured benchmarks have advanced text-conditional image generation for real-world imagery, however, no such benchmark exists for synthetic radiograph generation. Despite being a…
ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training
Siyue Li, Yongcheng Yao, Junru Zhong +9
Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contr…
Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics
Yongcheng Yao, Weitian Chen
Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysi…
CartiMorph: a framework for automated knee articular cartilage morphometrics
Yongcheng Yao, Junru Zhong, Liping Zhang +2
We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, i…
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Junru Zhong, Yongcheng Yao, Donal G. Cahill +7
Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small d…