8 citations · 14 across the 16 of their papers we have counts for
19 papers
Representation: Geometry Supervised Representation Learning of Phenotypes via Counterfactual Reasoning for Medical VLMs
Hao Wang, Qiwei Zeng, Jinghao Lin +6
Medical vision-language models (VLMs) have shown increasing potential for radiological image interpretation. Medical VLMs encode radiological images into visual representations tha…
Geometry-Supervised Visual Representation Learning for Multi-Phenotype Lesion Interpretation in Medical VLMs
Hao Wang, Qiwei Zeng, Shuchang Ye +5
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation. However, these models still struggle to interpret multi-phenotype lesions w…
Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation
Songyue Han, Mingye Zou, Shuchang Ye +2
Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating clinician-formulated textual repor…
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
Qiwei Zeng, Hao Wang, Jinghao Lin +6
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation. However, their practical…
From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation
Shuchang Ye, Jinqiang Yu, Zhujun Xiao +6
Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external APIs, including adaptation t…
ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction
Hao Wang, Yupeng Xu, Jinghao Lin +5
Longitudinal ophthalmic imaging analysis is an essential step for prognosis prediction in ophthalmic diseases. However, AI-assisted prognosis models are challenged by follow-up seq…
CBT-Audio: Evaluating Audio Language Models for Patient-Side Distress Intensity Estimation in CBT Session Recordings
Qixuan Hu, Shuchang Ye, Xumou Zhang +8
Cognitive behavioural therapy is widely used to help patients understand and manage psychological distress. It is often delivered through spoken conversation, where therapists atte…
RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection
Shuchang Ye, Mingyuan Meng, Hao Wang +2
Anatomical structure labels for chest radiographs are essential for medical image segmentation and a broad range of downstream diagnostic tasks. However, annotating anatomy directl…
Beyond Calibration: Confounding Pathology Limits Foundation Model Specificity in Abdominal Trauma CT
Jineel H Raythatha, Shuchang Ye, Jeremy Hsu +1
Purpose: Translating foundation models into clinical practice requires evaluating their performance under compound distribution shift, where severe class imbalance coexists with he…
MRG-R1: Reinforcement Learning for Clinically Aligned Medical Report Generation
Pengyu Wang, Shuchang Ye, Usman Naseem +1
Medical report generation aims to automatically produce radiology-style reports from medical images, supporting efficient and accurate clinical decision-making.However, existing ap…
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
Shuchang Ye, Usman Naseem, Mingyuan Meng +1
Medical language-guided segmentation, integrating textual clinical reports as auxiliary guidance to enhance image segmentation, has demonstrated significant improvements over unimo…
MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs
Pengyu Wang, Shuchang Ye, Usman Naseem +1
Medical Large Vision-Language Models (Med-LVLMs) have been widely adopted for medical report generation. Despite Med-LVLMs producing state-of-the-art performance, they exhibit a bi…
MedCFVQA: A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering
Shuchang Ye, Usman Naseem, Mingyuan Meng +2
Medical Visual Question Answering (MedVQA) is crucial for enhancing the efficiency of clinical diagnosis by providing accurate and timely responses to clinicians' inquiries regardi…
MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction
Andrew Zhang, Hao Wang, Shuchang Ye +2
Patient motion during medical image acquisition causes blurring, ghosting, and distorts organs, which makes image interpretation challenging. Current state-of-the-art algorithms us…
SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models
Mingxiao Tu, Shuchang Ye, Hoijoon Jung +1
Generating high-quality, photorealistic textures for 3D human avatars remains a fundamental yet challenging task in computer vision and multimedia field. However, real paired front…
Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation
Hao Wang, Shuchang Ye, Jinghao Lin +2
Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support. Large vision-language models (LVLMs) hold great pr…
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
Mingjian Li, Mingyuan Meng, Shuchang Ye +4
Medical image segmentation is a fundamental task in numerous medical engineering applications. Recently, language-guided segmentation has shown promise in medical scenarios where t…
SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
Shuchang Ye, Mingyuan Meng, Mingjian Li +2
Segmentation of infected areas in chest X-rays is pivotal for facilitating the accurate delineation of pulmonary structures and pathological anomalies. Recently, multi-modal langua…
Dynamic Traceback Learning for Medical Report Generation
Shuchang Ye, Mingyuan Meng, Mingjian Li +3
Automated medical report generation has demonstrated the potential to significantly reduce the workload associated with time-consuming medical reporting. Recent generative represen…