98 citations · 235 across the 29 of their papers we have counts for
31 papers
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
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…
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…
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…
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…
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…
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.…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…