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.
Don't be hesitant to drop me an email if you are interested in my work, want a collaboration and/or want to know more about me.

Selected Publications

Sparse Convolutional Neural Networks for Medical Image Analysis. Preprint
[Demonstration] Li, J., Gsaxner C., Pepe, A. et al.

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
Li, J. and Egger, J.

Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality. In VRST '21: Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology. [Presentation] (Gsaxner, C.)
Gsaxner, C. Li, J. Pepe, A., et al.

MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants. In Data in Brief 2021. [dataset]
Li, J., Krall, M., Trummer, F., Memon, A.R., Pepe, A., Gsaxner, C et al.

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation. In Arxiv August 2021. [github]
Li, J., Pepe, A., Gsaxner, C., 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.

Towards the Automatization of Cranial Implant Design in Cranioplasty (AutoImplant 2020 Proceedings)
First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings.
(57 total citations as of December,2021.) Li, J. and Egger, J(Eds.)

Medical image segmentation in oral-maxillofacial surgery (Book Chapter)
In Computer-Aided Oral and Maxillofacial Surgery. pp.1-27.
Li, J., Erdt, M., Janoos, F., Chang, T.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.(Joint 1st author), 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.

Synthetic skull bone defects for automatic patient-specific craniofacial implant design.In Scientific Data 8, no. 1 (2021): 1-8. [ dataset], [Github]
Li, J., Gsaxner, C., Pepe, A., Morais, A., Alves, V., von Campe, G., Wallner, J. and Egger, J.

Structural Analysis of Complex Atrial Intramural Microstructure from A Multi-layer Model Based on Siamese Network. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 294-297). IEEE. (Interpretable AI in Medicine)
Li, J., Chen, R. and Wu, J.

Accepted Proposals
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