Dr. Amirreza Mahbod

AI Researcher at MIAAI at Danube Private University


Amirreza Mahbod obtained his BSc and first MSc degrees in electrical engineering from the Iran University of Science and Technology, Tehran, Iran. He also received a second MSc in biomedical engineering from the KTH Royal Institute of Technology, Stockholm, Sweden. He got his PhD in 2020 from the Medical University of Vienna, Austria, where he served as an industrial PhD fellow, working jointly at the Medical University of Vienna and TissueGnostics GmbH. For the PhD thesis, he mainly worked on the segmentation and classification of various structures and tissues in microscopic images. Since 2020, he has been a postdoctoral fellow at the Institute of Pathophysiology and Allergy Research at the Medical University of Vienna. He will start his new role as an AI researcher at the Medical Image Analysis & Artificial Intelligence group at Danube Private University in August 2022. His main research interest are medical image analysis, computer vision, machine learning and deep learning, where he published several articles in peer-reviewed journals and conferences. He is particularly interested in developing novel deep learning-based methods for histological image analysis.

Key publications

Selected peer-reviewed articles (the full list is available on Google Scholar):

Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.

Mahbod A, Schaefer G, Löw C, Dorffner G, Ecker R, Ellinger I.

Diagnostics. 2021 Jun;11(6):967.


CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images.

Mahbod A, Schaefer G, Bancher B, Löw C, Dorffner G, Ecker R, Ellinger I.

Computers in Biology and Medicine. 2021 May 1;132:104349.


The effects of skin lesion segmentation on the performance of dermatoscopic image classification.

Mahbod A, Tschandl P, Langs G, Ecker R, Ellinger I.

Computer Methods and Programs in Biomedicine. 2020 Dec 1;197:105725.


Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification.

Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I.

Computer Methods and Programs in Biomedicine. 2020 Sep 1;193:105475.


A two-stage U-Net algorithm for segmentation of nuclei in H&E-stained tissues.

Mahbod A, Schaefer G, Ellinger I, Ecker R, Smedby Ö, Wang C.

InEuropean Congress on Digital Pathology 2019 Apr 10 (pp. 75-82). Springer, Cham.


Fusing fine-tuned deep features for skin lesion classification.

Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C.

Computerized Medical Imaging and Graphics. 2019 Jan 1;71:19-29.


Automatic brain segmentation using artificial neural networks with shape context.

Mahbod A, Chowdhury M, Smedby Ö, Wang C.

Pattern Recognition Letters. 2018 Jan 1;101:74-9.