Wenxuan Li

I am a Ph.D. student in the Department of Computer Science at Johns Hopkins University, co-advised by Bloomberg Distinguished Professor Prof. Alan L. Yuille, and Prof. Zongwei Zhou. I am a member of the CCVL group. Previously, I received my M.S.E. in Computer Science from Johns Hopkins University in 2023, and graduated Cum Laude with a B.Sc. in Computer Science from the University of Delaware in 2021.

My research focuses on medical computer vision and AI for healthcare, with particular emphasis on building large-scale, well-annotated 3D medical imaging datasets and developing AI algorithms for early cancer detection.

Email / CV / Google Scholar / GitHub / LinkedIn

Awards and Honors

CVPR 2026 Outstanding Reviewer, 2026 (top 5% in 17,491 reviewers)
Third Place, 2026 Johns Hopkins Healthcare Design Competition, 2026 (9 out of 568)
Runner-up, MICCAI Best Paper Award, 2025 (2 out of 1027)
First Place, Medical Segmentation Decathlon (MSD) Competition, 2025
Finalist, Whiting School of Engineering Trainee Award, Johns Hopkins University, 2025

Selected Publications

	PanTS: The Pancreatic Tumor Segmentation Dataset Wenxuan Li^, Xinze Zhou^, Qi Chen^, Tianyu Lin, Pedro R. A. S. Bassi, Xiaoxi Chen, Chen Ye, Zheren Zhu, Kai Ding, Heng Li, Kang Wang, Yang Yang, Yucheng Tang, Daguang Xu, Alan L. Yuille, Zongwei Zhou Conference on Neural Information Processing Systems (NeurIPS), 2025 3rd Place, Johns Hopkins Healthcare Design Competition'26 paper / code / data A large-scale dataset and benchmark for pancreatic tumor segmentation. ^ Equal contribution.
	Learning Segmentation from Radiology Reports Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen, Zheren Zhu, Tianyu Lin, Sergio Decherchi, Andrea Cavalli, Kang Wang, Yang Yang, Alan L. Yuille, Zongwei Zhou Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025. Runner-up, Best Paper Award (2 out of 1027) paper / code Leverages free-text radiology reports as weak supervision to train accurate medical segmentation models.
	Scaling Laws in Tumor Segmentation: Best Lessons from Real and Synthetic Data Qi Chen, Xinze Zhou, Chen Liu, Hao Chen, Zekun Jiang, Wenxuan Li, Ziyan Huang, Yuxuan Zhao, Dexin Yu, Junjun He, Yefeng Zheng, Ling Shao, Alan L. Yuille, Zongwei Zhou International Conference on Computer Vision (ICCV), 2025 paper / code Investigates scaling behavior of tumor segmentation models with combined real and synthetic data.
	RadGPT: Constructing 3D Image-Text Tumor Datasets Pedro R. A. S. Bassi, Mehmet Can Yavuz, Kang Wang, Xiaoxi Chen, Wenxuan Li, Sergio Decherchi, Andrea Cavalli, Yang Yang, Alan L. Yuille, Zongwei Zhou International Conference on Computer Vision (ICCV), 2025 paper / code / data Builds large-scale 3D image-text tumor datasets to power radiology language models.
	Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? Wenxuan Li^, Pedro R. A. S. Bassi^, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Saikat Roy, Yannick Kirchhoff, Maximilian Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Klaus H. Maier-Hein, Paul Jaeger, et al., Alan L. Yuille, Zongwei Zhou Conference on Neural Information Processing Systems (NeurIPS), 2024 paper / code A rigorous, multi-institutional benchmark for medical image segmentation algorithms. ^* Equal contribution. Authors are permitted to list their name first in their CVs.
	AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Domain Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking Wenxuan Li, Chongyu Qu, Xiaoxi Chen, Pedro R. A. S. Bassi, Yijia Shi, Yuxiang Lai, Qian Yu, Huimin Xue, Yixiong Chen, Xiaorui Lin, Yutong Tang, Yining Cao, Haoqi Han, Zheyuan Zhang, Jiawei Liu, Tiezheng Zhang, Yujiu Ma, Jincheng Wang, Guang Zhang, Alan L. Yuille, Zongwei Zhou Medical Image Analysis, 2024 paper / code / data The largest publicly-released abdominal CT dataset for transfer learning and algorithm benchmarking.
	A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties Junfei Xiao, Ziqi Zhou, Wenxuan Li, Shiyi Lan, Jieru Mei, Zhiding Yu, Bingchen Zhao, Alan L. Yuille, Yuyin Zhou, Cihang Xie European Conference on Computer Vision (ECCV), 2024 paper / code Strengthens segmentation models by enriching the semantic space with descriptive language properties.
	How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks? Wenxuan Li, Alan L. Yuille, Zongwei Zhou International Conference on Learning Representations (ICLR), 2024. Oral (top 1.2%) paper / code / data Introduces AbdomenAtlas 1.1, one of the largest fully-annotated 3D CT datasets, and SuPreM, a suite of supervised pre-trained 3D models for medical image segmentation.

Professional Services

Workshop Organizer: 12th CVPR Workshop on Medical Computer Vision, CVPR'26
Challenge Organizer:
Body Maps: Towards 3D Atlas of Human Body, ISBI'24 & MICCAI'24
Challenge for Vision-Language Modeling in 3D Medical Imaging, MICCAI'25 & ICCV'25
Reviewer: CVPR, MICCAI

Press Coverage

JHU WSE News: Hold on to your PanTS—there's a new pancreatic cancer detection dataset in town, 2025
JHU CS News: CCVL researchers to present 18 abstracts at RSNA 2025, 2025
JHU Malone Center News: For AI tumor detection, a picture isn't always worth a thousand words, 2025
Johns Hopkins Engineering Magazine: Gut Check, 2025
JHU News-Letter: AbdomenAtlas: an AI-based approach for early cancer diagnosis, 2025
JHU CS News: A touchstone of medical artificial intelligence, 2025
JHU News: AI-Powered Map of the Abdomen Could Help Find Cancer Early On, 2025
JHU WSE News: AI and Radiologists Unite to Map the Abdomen, 2024

Design and source code from Jon Barron's website.