1 citations · 1 across the 4 of their papers we have counts for
5 papers
Knee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment
Maryam Baizhigitova, Andrew Seohwan Yu, Po-Hao Chen +7
Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited, particularly for interpreting…
A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning
Pegah Ahadian, Mingrui Yang, Sixu Chen +2
Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This mismatch complicates clinical int…
Medical Image Spatial Grounding with Semantic Sampling
Andrew Seohwan Yu, Mohsen Hariri, Kunio Nakamura +3
Vision language models (VLMs) have shown significant promise in visual grounding for images as well as videos. In medical imaging research, VLMs represent a bridge between object d…
Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2
Andrew Seohwan Yu, Mohsen Hariri, Xuecen Zhang +3
Intelligent medical image segmentation methods are rapidly evolving and being increasingly applied, yet they face the challenge of domain transfer, where algorithm performance degr…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…