Jun Li - Ph.D. Student at Technical University of Munich

Jun Li

​Ph.D. Student
Technical University of Munich, Munich Center for Machine Learning
Hobbies: 🛹🎹🚴‍♀️🎧🏋️👩‍💻
🌟 Keep your eyes on the stars, and your feet on the ground.🤞🏻

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News

[09.2025]  Our paper NOVA Benchmark accepted as Oral paper by NeurIPS 2025 Datasets and Benchmarks Track!

[09.2025]  Our paper K2Sight received Early Accept by WACV 2026 (Top 6.4%)!

[09.2025]  Invited by ACM Computing Surveys as reviewer (Impact Factor: 39.89).

[08.2025]  Honorable Mention: MICCAI 2025 Outstanding Reviewer Award! See MICCAI 2025 Outstanding Reviewer Awards.

[04.2025]  My new seminar 📚 AI for Vision-Language Models in Medical Imaging (IN2107, IN45069) is now open! For more details, please visit the course GitHub repository.

[09.2023]  My seminar 📘 AI for Vision-Language Pre-training in Medical Imaging (IN2107) is now open! For more details, please visit the course GitHub repository.

[03.2024]  Started PhD studies at Technical University of Munich under the supervision of Prof. Julia Schnabel.

[07.2023]  Completed Master's degree at University of Chinese Academy of Sciences.

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Short Bio

​I am currently a Ph.D. student in the School of Computation, Information and Technology,  Technical University of Munich, supervised by Prof. Julia Schnabel. I am funded by the Munich Center for Machine Learning (MCML). Previously, I received the M. Eng. degree from University of the Chinese Academy of Sciences , under the supervison from Prof. Ying Hu.

My research focused on the intersection of deep learning and healthcare, particularly in the analysis of medical images. My passion lies in improving the practicality of deep learning algorithms, with a primary focus on Vision and Language models, Cross-Modality Generation, and Multi-Modality Learning. Through my work in these areas, I aim to advance deep learning techniques and their transformative impact on healthcare.

Publications
ReEvalMed paper
ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment

Ruochen Li*, Jun Li*, Bailiang Jian, Kun Yuan, Youxiang Zhu

[paper]

DINOv3 paper
Does DINOv3 Set a New Medical Vision Standard?

Che Liu, Yinda Chen, Haoyuan Shi, Jinpeng Lu, Bailiang Jian, Jiazhen Pan, Linghan Cai, Jiayi Wang, Yundi Zhang, Jun Li, Cosmin I. Bercea, Cheng Ouyang, Chen Chen, Zhiwei Xiong, Benedikt Wiestler, Christian Wachinger, Daniel Rueckert, Wenjia Bai, Rossella Arcucci

[paper]

K2Sight paper
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding

Accepted by WACV 2026 (Early Accept, Top 6.4%).

Jun Li, Che Liu, Wenjia Bai, Mingxuan Liu, Rossella Arcucci, Cosmin I. Bercea, Julia A. Schnabel

[paper] [homepage]

NOVA paper
NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI

Accepted as Oral paper by NeurIPS 2025 Datasets and Benchmarks Track.

Cosmin I. Bercea, Jun Li, Philipp Raffler, Evamaria O. Riedel, Lena Schmitzer, Angela Kurz, Felix Bitzer, Paula Roßmüller, Julian Canisius, Mirjam L. Beyrle, Che Liu, Wenjia Bai, Bernhard Kainz, Julia A. Schnabel, Benedikt Wiestler.

[paper] [huggingface]

AG-KD paper
Enhancing Abnormality Grounding for Vision-Language Models with Knowledge Descriptions

Jun Li, Che Liu, Wenjia Bai, Rossella Arcucci, Cosmin I. Bercea, Julia A. Schnabel.

[paper] [project] [huggingface]

OUI paper
Organizing Unstructured Image Collections using Natural Language

Mingxuan Liu, Zhun Zhong, Jun Li, Gianni Franchi, Subhankar Roy, Elisa Ricci.

[paper]

FMBench paper
Fmbench: Benchmarking fairness in multimodal large language models on medical tasks

Peiran Wu, Che Liu, Canyu Chen, Jun Li, Cosmin I Bercea, Rossella Arcucci.

[paper]

MI-VQA paper
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability

Accepted by MMMI 2024.

Jun Li, Su Hwan Kim, Philip Mller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, Cosmin I. Bercea.

[paper] [project]

DSD paper
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training

Tongkun Su*, Jun Li*, Xi Zhang, Haibo Jin, Hao Chen, Qiong Wang, Faqin Lv, Baoliang Zhao, Yin Hu

[paper] [Code]

URG paper
Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance

IEEE Transactions on Medical Imaging (IF:10.6).

Jun Li, Tongkun Su, Baoliang Zhao, Faqin Lv, Qiong Wang, Nassir Navab, Ying Hu, Zhongliang Jiang.

[paper] [project]

SGF paper
A Self-guided Framework for Radiology Report Generation

Accepted by MICCAI 2022. (Early Accept)
(Student Travel Award, Top 5%)

Jun Li, Shibo Li, Ying Hu, Huiren Tao.

[paper] [project]

XctNet paper
XctNet: Reconstruction network of volumetric images from a single X-ray image

Computerized Medical Imaging and Graphics (CMIG), 2022.

Zhiqiang Tan, Jun Li, Huiren Tao, Shibo Li, Ying Hu.

[paper]

Projects
Reconstruct 3D CT from X-rays
Reconstruct 3D CT from X-rays
  • This project focuses on exploring techniques for reconstructing 3D CT images from 2D X-rays.
  • Project worked at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
Github Repo
Cobb Angle Project
Spine Cobb Angle Measurement
  • Automatic and semi-automatic Cobb angle measurement tool for spinal X-ray images (coronal & sagittal planes), implemented with OpenCV + NumPy.
  • Project worked at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
Github Repo
AG-KD Project
Text to Image Generation
  • Using GAN to generate ultrasound scan from ultrasound repots.
  • Project worked at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
Github Repo
Services