Mina Rezaei


I am Postdoc at the Department of Statistics at the Ludwig-Maximilians-University Munich (LMU) in the working group Computational Statistics. Previously, I have been a machine learning/deep learning researcher at Hasso-Plattner Institute (HPI), Potsdam University from November 2015 to June 2019 for my Ph.D. studies under the supervision of Prof. Dr. Christoph Meinel. I did my M.Sc. degree in Artificial Intelligence at the Department of Computer Science, Shiraz University and Bachelor's degree in Computer Science, Software Engineering.

My Ph.D. research was focused on both technical and theoretical skills of the machine learning, by developing various algorithms on deep learning and generative adversarial networks to handle class imbalanced problem in medical imaging for diseases diagnosis and segmentation of abnormal tissues as well as body organs such as voxel-GAN, Recurrent-GAN, Ensemble GANs, and Mutual-GAN. I developed Bayesian Ensemble GANs during a Ph.D. internship at 3D imaging research lab at Harvard Medical School and Massachusetts General Hospital for 6 months before joining LMU.


Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Mina.Rezaei [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

Selected Publications

  1. Rezaei M, Uemura T, Näppi J, Yoshida H, Lippert C, Meinel C (2020) Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis Medical Imaging 2020: Computer-Aided Diagnosis, p. 113140E. International Society for Optics and Photonics.
  2. Rezaei M, Yang H, Meinel C (2019) Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks Medical Imaging 2019: Computer-Aided Diagnosis, p. 1095027. International Society for Optics and Photonics.
  3. Rezaei M, Yang H, Harmuth K, Meinel C (2019) Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1836–1845. IEEE.
  4. Rezaei M, Yang H, Meinel C (2019) Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimedia Tools and Applications, 1–20.
  5. Rezaei M, Yang H, Meinel C (2019) voxel-gan: Adversarial framework for learning imbalanced brain tumor segmentation. BrainLes 2018. Springer LNCS 11384, 321–333.
  6. Rezaei M, Yang H, Meinel C (2018) Instance tumor segmentation using multitask convolutional neural network 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE.
  7. Bakas S, Reyes M, Jakab A et al. (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.
  8. Rezaei M, Harmuth K, Gierke W et al. (2017) A conditional adversarial network for semantic segmentation of brain tumor International MICCAI Brainlesion Workshop, pp. 241–252. Springer.