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Mina Rezaei

19 papers hereh-index 163.3k citations38 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author4
  • last author5

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.LG3
  • stat.ML2
  • cs.CL1
  • cs.HC1
  • eess.IV1
same name
  • Mina Rezaei — 5 papers, h 5
  • Mina Rezaei — 4 papers, h 2
  • Mina Rezaei — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedDeep Neural Network with l2-norm Unit for Brain Lesions Detection

33 citations · 103 across the 14 of their papers we have counts for

collaborators
Showing 2017 · cs.CVShow all

4 papers · 2 filters

cs.CV2017★ 1 cited

Deep Learning for Medical Image Analysis

Mina Rezaei, Haojin Yang, Christoph Meinel

This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical…

cs.CV2017★ 32 cited

Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

Mina Rezaei, Konstantin Harmuth, Willi Gierke +4

Automated medical image analysis has a significant value in diagnosis and treatment of lesions. Brain tumors segmentation has a special importance and difficulty due to the differe…

cs.CV2017★ 33 cited

Deep Neural Network with l2-norm Unit for Brain Lesions Detection

Mina Rezaei, Haojin Yang, Christoph Meinel

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Lea…

cs.CV2017★ 4 cited

Brain Abnormality Detection by Deep Convolutional Neural Network

Mina Rezaei, Haojin Yang, Christoph Meinel

In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.…

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