Jianning Li



My research interests include computer vision, maching learning (incl. interpretable deep learning, disentangled representation learning, deep generative models etc), and their applications in medical image analysis. I am also interested in mathematical vision and neural models. Write me an email via jianningli.me@gmail.com if you want to discuss ideas.



Notice
Recent Publications
(Co-)editted Conference Proceedings

Shape in Medical Imaging . International Workshop, ShapeMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings.

Medical Applications with Disentanglements. First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings.

Towards the Automatization of Cranial Implant Design in Cranioplasty II. Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings.

Towards the Automatization of Cranial Implant Design in Cranioplasty. First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings.

Selected Publications

Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder . In Medical Applications with Disentanglements. MAD 2022. Springer, Cham 2022. [arxiv] [Github][Poster] Li, J., Fragemann, J. et al.

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution. In Scientific Reports [Demonstration] Li, J., Gsaxner C., Pepe, A. et al.

AutoImplant 2020 -First MICCAI Challenge on Automatic Cranial Implant Design. In IEEE Transactions on Medical Imaging (TMI) 2021. DOI:10.1109/TMI.2021.3077047. [github][Bibtex]
Li, J., Pimentel, P., Szengel, A., Ehlke, M., Lamecker, H., et al.

A Baseline Approach for AutoImplant: The MICCAI 2020 Cranial Implant Design Challenge. In Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, pp. 75-84. Springer, Cham, 2020. [Project Page]
Li, J., Pepe, A., Gsaxner, C., von Campe, G. and Egger, J.

An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design . In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598, p. 115981Q. International Society for Optics and Photonics, 2021. [Project Page]
Li, J., Pepe, A., Gsaxner, C. and Egger, J.

Detection, segmentation, simulation and visualization of aortic dissections: A review. In Medical image analysis (MedIA), 65, p.101773.
Pepe, A*., Li, J* (*Equal contribution), Rolf-Pissarczyk, M., Gsaxner, C., Chen, X., Holzapfel, G.A. and Egger, J

Automatic Skull Defect Restoration and Cranial Implant Generation for Cranioplasty. In Medical image analysis (MedIA) 2021. [Github]
Li, J., von Campe, G., Pepe, A., Gsaxner, C. et al.

Professional Service
Visitor Map