10 citations · 25 across the 15 of their papers we have counts for
15 papers
EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval
Jiashi Lin, Changhong Jiang, Xiangru Lin +12
Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introd…
MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline
Jiyao Liu, Jianghan Shen, Sida Song +19
Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medicine, developing clinical guid…
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
Jinlun Ye, Jiang Liao, Runhe Lai +4
Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representations. Recent CLIP-based test-ti…
MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
Jiyao Liu, Jinjie Wei, Wanying Qu +17
Medical Image Quality Assessment (IQA) serves as the first-mile safety gate for clinical AI, yet existing approaches remain constrained by scalar, score-based metrics and fail to r…
UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis
Junzhi Ning, Wei Li, Cheng Tang +24
Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing sy…
S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything without Supervision
Huihui Xu, Jin Ye, Hongqiu Wang +10
Recent self-supervised image segmentation models have achieved promising performance on semantic segmentation and class-agnostic instance segmentation. However, their pretraining s…
RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captions
Junzhi Ning, Cheng Tang, Kaijing Zhou +12
The scarcity of high-quality, labelled retinal imaging data, which presents a significant challenge in the development of machine learning models for ophthalmology, hinders progres…
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Junlong Cheng, Bin Fu, Jin Ye +10
Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model gen…
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation
Jin Ye, Ying Chen, Yanjun Li +7
Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric med…
GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI
Tianbin Li, Yanzhou Su, Wei Li +15
Despite significant advancements in general AI, its effectiveness in the medical domain is limited by the lack of specialized medical knowledge. To address this, we formulate GMAI-…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
Adrito Das, Danyal Z. Khan, Dimitrios Psychogyios +29
The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a sur…
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
Pengcheng Chen, Jin Ye, Guoan Wang +15
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medic…
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Guoan Wang, Jin Ye, Junlong Cheng +5
Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…