AI Researcher at MIAAI at Danube Private University
"Ass.-Prof. Dr. Amirreza Mahbod holds a BSc and two MSc degrees in electrical engineering, which he earned at the Iran University of Science and Technology in Tehran, Iran. Additionally, he obtained a second MSc degree in biomedical engineering from the KTH Royal Institute of Technology in Stockholm, Sweden. In 2020, he successfully completed his Ph.D. studies at the Medical University of Vienna, Austria, where he also served as an industrial PhD fellow. During this period, he conducted collaborative research at the Medical University of Vienna and TissueGnostics GmbH. His doctoral research primarily focused on the segmentation and classification of various structures and tissues in microscopic images.
Following the completion of his Ph.D., from 2020 to 2022, he served as a postdoctoral fellow at the Institute of Pathophysiology and Allergy Research at the Medical University of Vienna. He joined the Medical Image Analysis & Artificial Intelligence group at Danube Private University as an AI researcher, and since September 2023, he has been appointed as an assistant professor.
His primary research interests encompass a wide array of topics, including medical image analysis, computer vision, machine learning, and deep learning. His scholarly contributions extend to several peer-reviewed journals and conferences, and he also has played a significant role in securing successful grant applications. 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):
Amirreza Mahbod, Georg Dorffner, Isabella Ellinger, Ramona Woitek, Sepideh Hatamikia
ELSEVIER Computational and Structural Biotechnology Journal 23 (2024) 669–678
Received 12 September 2023; Received in revised form 26 December 2023; Accepted 26 December 2023
A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images
Mahbod A, Schaefer G, Hatamikia S., Dorffner G, Ecker R, Ellinger I.
Medicine Journal 2022 Nov 11;
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.