2 citations · 6 across the 15 of their papers we have counts for
16 papers
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Jiaxiang Liu, Tianxiang Hu, Juwei Guan +5
Recent advances in vision-language models (VLMs) such as CLIP have demonstrated strong generalization across natural-image domains. However, adapting these models to biomedical ima…
HICT: High-precision 3D CBCT reconstruction from a single X-ray
Wen Ma, Jiaxiang Liu, Zikai Xiao +3
Accurate 3D dental imaging is vital for diagnosis and treatment planning, yet CBCT's high radiation dose and cost limit its accessibility. Reconstructing 3D volumes from a single l…
MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Feng Guo, Jiaxiang Liu, Yang Li +2
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing publ…
Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation
Yuan Wang, Shujian Gao, Jiaxiang Liu +6
Automatic medical report generation can greatly reduce the workload of doctors, but it is often unreliable for real-world deployment. Current methods can write formally fluent sent…
Modest-Align: Data-Efficient Alignment for Vision-Language Models
Jiaxiang Liu, Yuan Wang, Jiawei Du +3
Cross-modal alignment aims to map heterogeneous modalities into a shared latent space, as exemplified by models like CLIP, which benefit from large-scale image-text pretraining for…
DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice
Zijie Meng, Jin Hao, Xiwei Dai +20
Diagnosing and managing oral diseases necessitate advanced visual interpretation across diverse imaging modalities and integrated information synthesis. While current AI models exc…
Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
Ziye Deng, Ruihan He, Jiaxiang Liu +5
Medical image grounding aims to align natural language phrases with specific regions in medical images, serving as a foundational task for intelligent diagnosis, visual question an…
GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation
Tianxiang Hu, Chenyi Zhou, Jiaxiang Liu +6
Cell type annotation is a fundamental step in the analysis of single-cell RNA sequencing (scRNA-seq) data. In practice, human experts often rely on the structure revealed by princi…
V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis
Yuan Wang, Jiaxiang Liu, Shujian Gao +5
Recent advances in multimodal techniques have led to significant progress in Medical Visual Question Answering (Med-VQA). However, most existing models focus on global image featur…
3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks
Xiaotang Gai, Jiaxiang Liu, Yichen Li +3
Medical Visual Question Answering (Med-VQA) holds significant potential for clinical decision support, yet existing efforts primarily focus on 2D imaging with limited task diversit…
Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
Peiran Wang, Linjie Tong, Jiaxiang Liu +1
Fairness is a fundamental principle in medical ethics. Vision Language Models (VLMs) have shown significant potential in the medical field due to their ability to leverage both vis…
KPL: Training-Free Medical Knowledge Mining of Vision-Language Models
Jiaxiang Liu, Tianxiang Hu, Jiawei Du +3
Visual Language Models such as CLIP excel in image recognition due to extensive image-text pre-training. However, applying the CLIP inference in zero-shot classification, particula…
MedCoT: Medical Chain of Thought via Hierarchical Expert
Jiaxiang Liu, Yuan Wang, Jiawei Du +2
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the rea…
PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction
Yujing Xue, Jiaxiang Liu, Jiawei Du +1
Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering bette…
R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest
Xupeng Chen, Zhixin Lai, Kangrui Ruan +3
Artificial intelligence has made significant strides in medical visual question answering (Med-VQA), yet prevalent studies often interpret images holistically, overlooking the visu…
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale
Xiaotang Gai, Chenyi Zhou, Jiaxiang Liu +3
Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcar…