collaborators

11 papers

cs.CV2026

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Zhongying Deng, Cheng Tang, Ziyan Huang +124

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…

cs.CL2026

Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data

Xinlin Zhuang, Feilong Tang, Haolin Yang +9

Supervised Fine-Tuning (SFT) of the language backbone plays a pivotal role in adapting Vision-Language Models (VLMs) to specialized domains such as medical reasoning. However, exis…

cs.CV2026

ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering

Zhaodong Wu, Haochen Xue, Qi Cao +5

Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable f…

q-bio.GN2025

Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging

Huifa Li, Feilong Tang, Haochen Xue +5

Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological dec…

cs.LG2025

scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering

Huifa Li, Jie Fu, Xinlin Zhuang +6

Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due…

cs.CL2025

TAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification

Jianghao Wu, Feilong Tang, Yulong Li +5

Recent advances such as Chain-of-Thought prompting have significantly improved large language models (LLMs) in zero-shot medical reasoning. However, prompting-based methods often r…