63 citations · 239 across the 76 of their papers we have counts for
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Ophiuchus: Incentivizing Tool-augmented "Think with Images" for Joint Medical Segmentation, Understanding and Reasoning
Yankai Jiang, Yujie Zhang, Peng Zhang +5
Recent medical MLLMs have made significant progress in generating step-by-step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate d…
MedSAM3: Delving into Segment Anything with Medical Concepts
Anglin Liu, Rundong Xue, Xu R. Cao +5
Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical a…
Versatile and Risk-Sensitive Cardiac Diagnosis via Graph-Based ECG Signal Representation
Yue Wang, Yuyang Xu, Renjun Hu +7
Despite the rapid advancements of electrocardiogram (ECG) signal diagnosis and analysis methods through deep learning, two major hurdles still limit their clinical adoption: the la…
NEF-NET+: Adapting Electrocardio panorama in the wild
Zehui Zhan, Yaojun Hu, Jiajing Zhan +3
Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals from a fixed set of anatomical viewpoints defined by lead placement. However, certain cardiac condit…
PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation
Yufei Wen, Shuxing Zhong, Jingdan Kang +3
Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical avail…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Lang Qin, Zijian Gan, Xu Cao +5
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion…
Strengthening LLMs for Tabular Prediction with Structural Priors
Pengxiang Cai, Zihao Gao, Wanchen Lian +2
Tabular prediction has long been dominated by gradient-boosted decision trees and specialized deep tabular models, while large language models (LLMs) remain difficult to make compe…
Icon: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation
Qiyuan Chen, Hongsen Huang, Qian Shao +6
Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant…
CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines
Qiyuan Chen, Jiahe Chen, Hongsen Huang +7
Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift…
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
Chufan Gao, Jintai Chen, Jimeng Sun
Automated tabular understanding and reasoning are essential tasks for data scientists. Recently, Large language models (LLMs) have become increasingly prevalent in tabular reasonin…
MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis
Yuting Zhang, Kaishen Yuan, Hao Lu +3
Accurate and interpretable multi-disease diagnosis remains a critical challenge in medical research, particularly when leveraging heterogeneous multimodal medical data. Current app…
STORM: Benchmarking Visual Rating of MLLMs with a Comprehensive Ordinal Regression Dataset
Jinhong Wang, Shuo Tong, Jian liu +6
Visual rating is an essential capability of artificial intelligence (AI) for multi-dimensional quantification of visual content, primarily applied in ordinal regression (OR) tasks…
Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models
Jiahe Chen, Jiaying He, Qiyuan Chen +6
Large Vision-Language Models (LVLMs) face a tug-of-war between powerful linguistic priors and visual evidence, often leading to \emph{semantic drift}: a progressive detachment from…
Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
Jintai Chen, Fangqing Liu, Xueming Yan +1
This study aims to optimize the evaluation metric of multimodal multi-objective optimization problems using a Regionalized Metric Framework, which provides a certain boost to resea…
OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLM
Jinhong Wang, Shuo Tong, Jian liu +7
Despite the remarkable progress of multimodal large language models (MLLMs), they continue to face challenges in achieving competitive performance on ordinal regression (OR; a.k.a.…
A Survey on Ordinal Regression: Applications, Advances and Prospects
Jinhong Wang, Jintai Chen, Jian Liu +3
Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image…
From Screens to Scenes: A Survey of Embodied AI in Healthcare
Yihao Liu, Xu Cao, Tingting Chen +7
Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and personalization. Powered by modern AI technologies such as multimodal large language model…
Foundation Model in Biomedicine
Xiangrui Liu, Yuanyuan Zhang, Qianyu Shang +14
Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive…
MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
Qian Shao, Bang Du, Zepeng Li +6
High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generatio…