Publications (34)
Interpretable Differential Diagnosis with Dual-Inference Large Language Models
Shuang Zhou, Mingquan Lin, Sirui Ding +4
Automatic differential diagnosis (DDx) is an essential medical task that generates a list of potential diseases as differentials based on patient symptom descriptions. In practice,…
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Hexin Dong, Yi Lin, Pengyu Zhou +25
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on cl…
LLM-as-a-Judge in Healthcare: A Scoping Analysis of Applications, Methods, and Human Alignment
Lingyao Li, Deyi Li, Chen Chen +9
Large language models (LLMs) are increasingly deployed across healthcare applications, including clinical documentation, diagnostic reasoning, medicine recommendation, and medical…
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
Gregory Holste, Mingquan Lin, Ruiwen Zhou +9
Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classificat…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional…
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
Shuang Zhou, Jiashuo Wang, Zidu Xu +11
Explainable disease diagnosis, which leverages patient information (e.g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear c…
MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis
Yiran Song, Yikai Zhang, Shuang Zhou +6
Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology, achieving strong diagnostic performance throu…
A survey of recent methods for addressing AI fairness and bias in biomedicine
Yifan Yang, Mingquan Lin, Han Zhao +3
Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reduci…
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application
Xueqing Peng, Lingfei Qian, Yan Wang +44
Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Tianrun Yu, Jiaqi Wang, Haoyu Wang +4
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate…
Continually Evolved Multimodal Foundation Models for Cancer Prognosis
Jie Peng, Shuang Zhou, Longwei Yang +7
Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data moda…
Concordia: Self-Improving Synthetic Tables for Federated LLMs
Jimin Huang, Duanyu Feng, Nuo Chen +8
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remai…
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
Mingquan Lin, Jacob Wynne, Yang Lei +4
Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This…
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Gregory Holste, Yiliang Zhou, Song Wang +22
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…
An empirical study of using radiology reports and images to improve ICU mortality prediction
Mingquan Lin, Song Wang, Ying Ding +3
Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality. Many sco…
Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning
Mingquan Lin, Tianhao Li, Zhaoyi Sun +5
Purpose: Limited studies exploring concrete methods or approaches to tackle and enhance model fairness in the radiology domain. Our proposed AI model utilizes supervised contrastiv…
Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow
Ziyang Xu, Mingquan Lin, Yiliang Zhou +6
Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermatopathology trainees. To establ…
A General Model for Retinal Segmentation and Quantification
Zhonghua Wang, Lie Ju, Sijia Li +12
Retinal imaging is fast, non-invasive, and widely available, offering quantifiable structural and vascular signals for ophthalmic and systemic health assessment. This accessibility…
A scoping review on multimodal deep learning in biomedical images and texts
Zhaoyi Sun, Mingquan Lin, Qingqing Zhu +4
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the…
Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
Zhongxing Xu, Zhonghua Wang, Zhe Qian +10
Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.…
Radiology Text Analysis System (RadText): Architecture and Evaluation
Song Wang, Mingquan Lin, Ying Ding +3
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads o…
Large Language Models for Disease Diagnosis: A Scoping Review
Shuang Zhou, Zidu Xu, Mian Zhang +14
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intellige…
AMD-Mamba: A Phenotype-Aware Multi-Modal Framework for Robust AMD Prognosis
Puzhen Wu, Mingquan Lin, Qingyu Chen +4
Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss, making effective prognosis crucial for timely intervention. In this work, we propose AMD-Mamb…
Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Hexin Dong, Yi Lin, Pengyu Zhou +7
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on cl…
Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis
Yiran Song, Yikai Zhang, Silvia Orengo-Nania +5
Glaucoma is one of the leading causes of irreversible blindness worldwide. Glaucoma prognosis is essential for identifying at-risk patients and enabling timely intervention to prev…
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
Guojun Xiong, Zhiyang Deng, Keyi Wang +10
Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle wi…
FinCriticalED: A Visual Benchmark for Financial Fact-Level OCR
Yueru He, Xueqing Peng, Yupeng Cao +13
Recent progress in multimodal large language models (MLLMs) has substantially improved document understanding, yet strong optical character recognition (OCR) performance on surface…
Deep learning with noisy labels in medical prediction problems: a scoping review
Yishu Wei, Yu Deng, Cong Sun +3
Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adop…
Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations
Mingquan Lin, Yuyun Xiao, Bojian Hou +6
In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widel…
Prior Knowledge Enhances Radiology Report Generation
Song Wang, Liyan Tang, Mingquan Lin +3
Radiology report generation aims to produce computer-aided diagnoses to alleviate the workload of radiologists and has drawn increasing attention recently. However, previous deep l…
Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective
Jun Wang, Zaifu Zhan, Qixin Zhang +3
Recent progress in large language models (LLMs) has leveraged their in-context learning (ICL) abilities to enable quick adaptation to unseen biomedical NLP tasks. By incorporating…
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
Yan Han, Chongyan Chen, Liyan Tang +7
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been…
PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities
Kai Yu, Shuang Zhou, Yiran Song +9
Multimodal self-supervised pretraining offers a promising route to cancer prognosis by integrating histopathology whole-slide images, gene expression, and pathology reports, yet mo…
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
Mingquan Lin, Gregory Holste, Song Wang +30
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease…