All papers that have not been peer-reviewed will not appear here, including preprints. You can access my all of papers at 🔗Google Scholar.
2025
Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Lin Zhao, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)
AAAI QIML 2025 Conference
We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models. Read more
Chatradio-valuer: A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data
Tianyang Zhong, Wei Zhao, Yutong Zhang, Yi Pan, Peixin Dong, Zuowei Jiang, Hanqi Jiang, Yifan Zhou, Xiaoyan Kui, Youlan Shang, Lin Zhao, Li Yang, Yaonai Wei, Zhuoyi Li, Jiadong Zhang, Longtao Yang, Hao Chen, Huan Zhao, Yuxiao Liu, Ning Zhu, Yiwei Li, Yisong Wang, Jiaqi Yao, Jiaqi Wang, Ying Zeng, Lei He, Chao Zheng, Zhixue Zhang, Ming Li, Zhengliang Liu, Haixing Dai, Zihao Wu, Lu Zhang, Shu Zhang, Xiaoyan Cai, Xintao Hu, Shijie Zhao, Xi Jiang, Xin Zhang, Wei Liu, Xiang Li†, Dajiang Zhu†, Lei Guo†, Dinggang Shen†, Junwei Han†, Tianming Liu†, Jun Liu†, Tuo Zhang†(† corresponding author)
IEEE Transactions on Biomedical Engineering 2025 Journal (IF=4.5)
A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data. A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data. Read more
MolQAE: Quantum Autoencoder for Molecular Representation Learning
Yi Pan*, Hanqi Jiang*, Wei Ruan, Dajiang Zhu, Xiang Li, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)
QAI 2025 Conference
Quantum Autoencoder for Molecular Representation Learning. Quantum Autoencoder for Molecular Representation Learning. Read more
Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models
Yifan Xu, Chao Zhang, Hanqi Jiang, Xiaoyan Wang, Ruifei Ma, Yiwei Li, Zihao Wu, Zeju Li, Xiangde Liu†(† corresponding author)
IEEE Transactions on Neural Networks and Learning Systems 2025 Journal (IF=10.2)
Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models. Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models. Read more
ECHOPulse: ECG Controlled Echocardiograms Video Generation
Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu†, Quanzheng Li†, Xiang Li†(† corresponding author)
ICLR 2025 Conference
We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG… We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPluse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Read more
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Yifan Zhou, Peng Shu, Zihao Wu, Zhengliang Liu, Dajiang Zhu, Xiang Li, Yohannes Abate, Tianming Liu†(* equal contribution)(† corresponding author)
ISBI 2025 Oral Conference
Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis. Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis. Read more
2024
ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling
Weihang You*, Hanqi Jiang*, Zishuai Liu, Zihang Xie, Tianming Liu, Jin Lu, Fei Dou†(* equal contribution)(† corresponding author)
Under Review 2025 Preprint
ADLGen synthesizes symbolic, event-triggered sensor sequences for human activity modeling, providing a novel approach to activity recognition and modeling. ADLGen synthesizes symbolic, event-triggered sensor sequences for human activity modeling, providing a novel approach to activity recognition and modeling. Read more
GeoDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation
Zhifei Yang, Keyang Lu, Chao Zhang, Jiaxing Qi, Hanqi Jiang, Ruifei Ma, Shenglin Yin, Yifan Xu, Mingzhe Xing, Zhen Xiao, Jieyi Long, Xiangde Liu†, Guangyao Zhai†(† corresponding author)
AAAI 2025 Conference
Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation. Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation. Read more
Artificial General Intelligence for Medical Imaging Analysis
Xiang Li, Lin Zhao, Lu Zhang, Zihao Wu, Zhengliang Liu, Hanqi Jiang, Chao Cao, Shaochen Xu, Yiwei Li, Haixing Dai, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu†, Dinggang Shen†(† corresponding author)
IEEE Reviews in Biomedical Engineering 2024 Feature Article Journal (IF=17.2)
A comprehensive review of artificial general intelligence for medical imaging analysis. A comprehensive review of artificial general intelligence for medical imaging analysis. Read more
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu†, Xiang Li†(† corresponding author)
NeurIPS 2024 Conference
We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks.… We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks. EGMA demonstrates that even a small amount of eye-gaze data can effectively assist in multi-modal pre-training and improve the feature representation ability of the model. Read more
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Hanqi Jiang, Xixuan Hao, Yuzhou Huang, Chong Ma, Jiaxun Zhang, Yi Pan, Ruimao Zhang†(† corresponding author)
ECCV Workshop 2024 Conference
We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED… We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED is capable of efficiently executing a variety of downstream tasks, including cross-modal, uni-modal, and multi-modal types. Extensive experimental results demonstrate that our HybridMED can deliver highly satisfactory performance across a wide array of downstream tasks, thereby validating the model's superiority. Read more
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
Hanqi Jiang, Cheng Zeng, Runnan Chen, Shuai Liang, Yinhe Han†, Yichao Gao, Conglin Wang(† corresponding author)
ICIC 2024 2024 Oral Conference
Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization. Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization. Read more
MFE-SSNet: Multi-Modal Fusion-Based End-to-End Steering Angle and Vehicle Speed Prediction Network
Yi Huang, Wenzhuo Liu, Yaoyu Li, Lei Yang, Hanqi Jiang, Zhiwei Li, Jun Li†(† corresponding author)
Automotive Innovation 2024 Journal (IF=6.1)
Multi-Modal Fusion-Based End-to-End Steering Angle and Vehicle Speed Prediction Network. Multi-Modal Fusion-Based End-to-End Steering Angle and Vehicle Speed Prediction Network. Read more