Artificial Intelligence; Pattern Recognition, Automated
I am an AI research scientist with expertise in medical image analysis, machine/deep learning, and computer vision.
I got my bachelor's (2004-2009) and first master's degree (2009-2012) in Electrical Engineering from the School of Electrical Engineering at the Iran University of Science and Technology, Tehran, Iran, and my second master's degree in Medical Engineering from the Division of Biomedical Imaging at the KTH Royal Institute of Technology, Stockholm, Sweden (2014-2016). I did my PhD (2016-2020), entitled "Towards Improvement of Automated Segmentation and Classification of Tissues and Nuclei in Microscopic Images Using Deep Learning Approaches", within a Marie Skłodowska-Curie European training network called CaSR Biomedicine (Horizon 2020 Framework) as an industrial PhD fellow at the Institute of Pathophysiology and Allergy Research at the Medical University of Vienna and at the Department of Research and Development at TissueGnostics GmbH.
Through a successful FFG grant application, I did a postdoc (2020-2022) within a collaborative project between the Medical Univerity of Vienna and TissueGnostics (project title: Deep learning-based knowledge transfer methods for nuclei segmentation in microscopic images)
Currently, I am employed as an AI researcher at Danube Private University (DPU) and a part-time lecturer at the Medical University of Vienna.
Techniques, methods & infrastructure
Techniques and Methods:
- Deep Convolutional Neural Networks
- Classical Machine Learning Methods (ANN, SVM, MLP, ...)
- Medical Image Analysis (Segmentation, Classification, Normalization, ...)
- Medical Imaging (Histopathology, Microscopy, MRI, CT, ...)
- Developing (Python, Keras, Tensorflow, PyTorch, Matlab)
- Mahbod, A. et al. (2022) ‘A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images’, Frontiers in Medicine, 9. Available at: http://dx.doi.org/10.3389/fmed.2022.978146.
- Mahbod, A. et al. (2022) ‘Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images’, Applications of Medical Artificial Intelligence, pp. 108–117. Available at: http://dx.doi.org/10.1007/978-3-031-17721-7_12.
- Mahbod, A. et al. (2021) ‘Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation’, Diagnostics, 11(6), p. 967. Available at: http://dx.doi.org/10.3390/diagnostics11060967.
- Mahbod, A. et al. (2021) ‘CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images’, Computers in Biology and Medicine, 132, p. 104349. Available at: http://dx.doi.org/10.1016/j.compbiomed.2021.104349.
- Mahbod, A. et al. (2020) ‘Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification’, Computer Methods and Programs in Biomedicine, 193, p. 105475. Available at: http://dx.doi.org/10.1016/j.cmpb.2020.105475.