Hasso-Plattner-Institut
 
    • de
 

Mina Rezaei

PhD student in Biomedical Image Analysis with Deep Learning

Machine Learning & Artificial Intelligence Research Group

Hasso-Plattner-Institut (HPI) für
Digital Engineering gGmbH
Universität Potsdam
Prof.-Dr.-Helmert-Str. 2-3
14482 Potsdam
Germany

office:      H-1.22
phone:    +49 (0)331-5509-538
fax:         +49 (0)331-5509-325
email:     mina.rezaei(at)hpi.de

LinkedIn profile

Research Interests

  • Deep Learning
  • Learning from Imbalanced Data
  • Generative Adversarial Models
  • Medical Image Analysis

Teaching

WS 2018/2019

SS 2018

WS 2017/2018

SS 2017

Talks

  • (26.10.2018): Mina Rezaei "GAN for Learning Imbalanced Medical Imaging", SAP next-gen, Berlin, Germany.
  • (18.10.2018): Mina Rezaei "Learning from Imbalanced Medical Imaging", HPI-Research School, Fall Retreat, Neuruppin, Germany.
  • (17.10.2018): Mina Rezaei "Understanding Medical Imaging Data Through Machine Learning", Big Data in Medicine Conference / HIMSS, Potsdam, Germany.
  • (27.05.2018): Mina Rezaei "Radimoc-GANs: Multi-Agnet Adversarial Framework for Learning Multiple Clinical Tasks", Dagstuhl, schloß Informatik, Germany.
  • (18.04.2018): Mina Rezaei "Survival-GAN: An Adversarial Framework for Multiple Clinical Tasks",13th Annual Symposium on Future Trends in Service-Oriented Computing, Potsdam, Germany.
  • (18.10.2017): Mina Rezaei "Multi-Agent Generative Adversarial Networks for learning  Medical Image Segmentation",HPI-Research School, Fall Retreat, Neuruppin, Germany.
  • (23.11.2017): Mina Rezaei "Context-aware of Multi-Agent Generative Adversarial Networks for learning Cardiac MRI segmentation",HPI-Research School NJU Workshop, Nanjing University, Nanjing, China.
  • (26.06.2017): Mina Rezaei "Hetrogenouse Brain Tumor Segmentation using Generative Adversarial Network",HPI-Research School Workshop, Cape town, South Africa.

Publications

2019

  • Mina RezaeiHaojin Yang, Konstantine Harmuth, Christoph Meinel: Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation. Accepted by IEEE Winter Conference on Application Computer Vision (WACV 2019, code)
  • Mina RezaeiHaojin YangChristoph Meinel: Learning Imbalanced Semantic Segmentation through Cross-Domain Relations of Multi-Agent Generative Adversarial Networks. Accepted by SPIE Medical Imaging - Computer Aided Diagnosis (SPIE 2019)

  2018

   2017

   2016