65 citations · 118 across the 19 of their papers we have counts for
21 papers
Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning
Kaitao Chen, Weiqian Zhao, Jiamin Wu +6
Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically exhi…
LUCID-SAE: Learning Unified Vision-Language Sparse Codes for Interpretable Concept Discovery
Difei Gu, Yunhe Gao, Gerasimos Chatzoudis +6
Sparse autoencoders (SAEs) offer a natural path toward comparable explanations across different representation spaces. However, current SAEs are trained per modality, producing dic…
Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers
Zhengjie Zhang, Xiaoxie Mao, Qihao Guo +5
Background: Alzheimer's disease (AD) diagnosis heavily relies on amyloid-beta positron emission tomography (Abeta-PET), which is limited by high cost and limited accessibility. Thi…
Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
Difei Gu, Yunhe Gao, Mu Zhou +1
Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image fe…
Think Twice to See More: Iterative Visual Reasoning in Medical VLMs
Kaitao Chen, Shaohao Rui, Yankai Jiang +6
Medical vision-language models (VLMs) excel at image-text understanding but typically rely on a single-pass reasoning that neglects localized visual cues. In clinical practice, how…
Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making
Kaitao Chen, Mianxin Liu, Daoming Zong +5
Complex medical decision-making involves cooperative workflows operated by different clinicians. Designing AI multi-agent systems can expedite and augment human-level clinical deci…
Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
Kexin Ding, Mu Zhou, Akshay Chaudhari +2
The wide exploration of large language models (LLMs) raises the awareness of alignment between healthcare stakeholder preferences and model outputs. This alignment becomes a crucia…
Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Yunhe Gao, Di Liu, Zhuowei Li +4
Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made signific…
MedForge: Building Medical Foundation Models Like Open Source Software Development
Zheling Tan, Kexin Ding, Jin Gao +4
Foundational models (FMs) have made significant strides in the healthcare domain. Yet the data silo challenge and privacy concern remain in healthcare systems, hindering safe medic…
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment
Difei Gu, Yunhe Gao, Yang Zhou +2
Automated chest radiographs interpretation requires both accurate disease classification and detailed radiology report generation, presenting a significant challenge in the clinica…
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
Kaitao Chen, Mianxin Liu, Fang Yan +8
The advent of vision-language models fosters the interactive conversations between AI-enabled models and humans. Yet applying these models into clinics must deal with daunting chal…
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
Yunhe Gao, Difei Gu, Mu Zhou +1
Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this s…
Data-Centric Foundation Models in Computational Healthcare: A Survey
Yunkun Zhang, Jin Gao, Zheling Tan +5
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models,…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
Text-guided Foundation Model Adaptation for Pathological Image Classification
Yunkun Zhang, Jin Gao, Mu Zhou +4
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with str…
Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction
Kexin Ding, Mu Zhou, Dimitris N. Metaxas +1
Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e.g., imaging and genomics biomarkers) in cancer. Enabling multimodal analytics…
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao, Zhuowei Li, Di Liu +3
A major focus of clinical imaging workflow is disease diagnosis and management, leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has…
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Qi Chang, Zhennan Yan, Mu Zhou +8
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patter…
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers
Di Liu, Yunhe Gao, Qilong Zhangli +8
Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment char…
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Qilong Zhangli, Jingru Yi, Di Liu +10
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, t…
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
Yunhe Gao, Mu Zhou, Di Liu +3
Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medic…