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Ivan Ezhov
Doctoral Student
Image-Based Biomedical Modeling Group (IBBM)
Fakultät für Informatik
Technische Universität München
Email: ivan.ezhov@tum.de
Office: TranslaTUM, Room No. 22.2.34
Students wanted
Multiple M.Sc/B.Sc thesis, guided research, IDP projects are available in Bayesian inference, generative modeling, federated learning for medical applications.
I am available 24/7 to discuss a potential project.
Publications
14. Geometry-aware neural solver for fast Bayesian calibration of brain tumor models, Ivan Ezhov et al., https://arxiv.org/abs/2009.04240
13. Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations, Hongwei Li, Fei-Fei Xue, Krishna Chaitanya, Shengda Liu, Ivan Ezhov et al., https://arxiv.org/abs/2103.04167
12. Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient, F Kofler, I Ezhov, F Isensee, F Balsiger, C Berger et al., https://arxiv.org/abs/2103.06205
11. A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images, Stefan Gerl, Johannes C Paetzold, Hailong He, Ivan Ezhov, Suprosanna Shit, Florian Kofler, ..., arXiv preprint arXiv:2007.05324
10. Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective,
AB Qasim*, I Ezhov*, S Shit, O Schoppe, JC Paetzold, A Sekuboyina, ...
arXiv preprint arXiv:2004.10734 2020
9. clDice--a Topology-Preserving Loss Function for Tubular Structure Segmentation,
S Shit, JC Paetzold, A Sekuboyina, A Zhylka, I Ezhov, A Unger, ...
arXiv preprint arXiv:2003.07311 2020
8. BraTS? Toolkit: Translating BraTS? brain tumor segmentation algorithms into clinical and scientific practice,
F Kofler, C Berger, D Waldmannstetter, J Lipkova, I Ezhov, G Tetteh, ...
Frontiers in Neuroscience 14, 125 2020
7. Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee,
S Shit, A Ravi, I Ezhov, J Lipkova, M Piraud, B Menze
NeurIPS? 2019 Workshop on Machine Learning with Guarantees 2019
6. A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information,
F Kofler, JC Paetzold, I Ezhov, S Shit, D Krahulec, JS Kirschke, C Zimmer, ...
International MICCAI Brainlesion Workshop, 254-261
5. clDice-a Novel Connectivity-Preserving Loss Function for Vessel Segmentation, J. Paetzold, S. Shit, I. Ezhov, G. Tetteh, A. Ertuerk, B. Menze, Medical Imaging Meets NeurIPS?? 2019 (Workshop), Vancouver, Canada, December 2019
4. Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation, F. Navarro, S. Shit, I. Ezhov, J. Paetzold, A. Gafita, J. Peeken, S. Combs, B. Menze, Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), Shenzhen, China, October 2019.
3. Neural parameters estimation for brain tumor growth modeling, I. Ezhov, J. Lipkova, S. Shit, F. Kofler, N. Collomb, B. Lemasson, E. Barbier, B. Menze, Proceedings of the 22th International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI), Shenzhen, China, October 2019
2. Influence of screening on longitudinal-optical phonon scattering in quantum cascade lasers, I Ezhov, C Jirauschek, Journal of Applied Physics 119 (3), 2016
1. Density matrix analysis of terahertz quantum cascade lasers for frequency comb generation, P Tzenov, I Ezhov, C Jirauschek, The 13th International Conference on Intersubband Transitions in Quantum Wells
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