8 citations · 18 across the 16 of their papers we have counts for
18 papers
MetaStructAtlas: A Grounded 3D Vision-Language Dataset and Benchmark for Functional and Structural Reasoning in Whole-Body PET/CT
Chenguang Zheng, Le Xue, Yichi Zhang +8
The joint interpretation of metabolic function and anatomical structure is essential for clinical diagnosis in whole-body PET/CT. Although recent advances in 3D medical vision-lang…
SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusion
Yichi Zhang, Le Xue, Bichun Xu +6
Semi-supervised learning (SSL) has become a promising solution to alleviate the annotation burden of deep learning-based medical image segmentation models. While recent advances in…
Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomography
Yichi Zhang, Le Xue, Wenbo Zhang +16
Positron emission tomography (PET) is a key nuclear medicine imaging modality that visualizes radiotracer distributions to quantify in vivo physiological and metabolic processes, p…
Universality Reconsidered: Rethinking the Validation of Foundation Models for General-Purpose 3D Medical Segmentation
Yichi Zhang, Le Xue, Feiyang Xiao +6
Foundation models have emerged as a transformative paradigm in 3D medical imaging, with the promise of unified quantitative analysis across diverse targets and imaging modalities.…
Finding Kissing Numbers with Game-theoretic Reinforcement Learning
Chengdong Ma, Théo Tao Zhaowei, Pengyu Li +7
Since Isaac Newton first studied the Kissing Number Problem in 1694, determining the maximal number of non-overlapping spheres around a central sphere has remained a defining chall…
PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography
Yichi Zhang, Wenbo Zhang, Zehui Ling +12
Positron emission tomography (PET) is a cornerstone of modern oncologic and neurologic imaging, distinguished by its unique ability to illuminate dynamic metabolic processes that t…
DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
Zhixin Wang, Jiaming Xu, Tianyi Zhou +10
Effectively scaling Reinforcement Learning (RL) is crucial for enhancing the reasoning and alignment of Large Language Models. The massive data and complex execution flows inherent…
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Kaiyu Guo, Tan Pan, Chen Jiang +5
Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy con…
AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
Xigui Li, Yuanye Zhou, Feiyang Xiao +18
Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape editing can expand limited geometry col…
ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
Yifeng Jiao, Yuchen Liu, Yu Zhang +9
The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembl…
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images
Yichi Zhang, Le Xue, Wenbo Zhang +5
Positron Emission Tomography (PET) is a powerful molecular imaging tool that plays a crucial role in modern medical diagnostics by visualizing radio-tracer distribution to reveal p…
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
Xigui Li, Yuanye Zhou, Feiyang Xiao +10
Intracranial aneurysm (IA) is a common cerebrovascular disease that is usually asymptomatic but may cause severe subarachnoid hemorrhage (SAH) if ruptured. Although clinical practi…
Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation
Zhaorui Tan, Xi Yang, Tan Pan +8
Variations in medical imaging modalities and individual anatomical differences pose challenges to cross-modality generalization in multi-modal tasks. Existing methods often concent…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
Xiaoyu Tan, Haoyu Wang, Xihe Qiu +4
Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs). Still, its integration poses a ch…
AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications
Xin Pang, Zhucong Li, Jiaxiang Chen +3
We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Appli…
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Yichi Zhang, Jin Yang, Yuchen Liu +2
Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is espec…
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Yichi Zhang, Shiyao Hu, Sijie Ren +3
The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation model for prompt-driven image segmentation tasks. However, both the original SAM and its medica…