Detail

Keywords

Artificial Intelligence; Pattern Recognition, Automated

Research interests

Amirreza Mahbod is an Associate Professor of Computational Pathology at the Research Center for Medical Image Analysis and Artificial Intelligence, Danube Private University, Austria, and a part-time lecturer at the Medical University of Vienna. He received his Ph.D. and habilitation in Medical Informatics, Biostatistics and Complex Systems from the Medical University of Vienna. His research focuses on artificial intelligence-based medical image and data analysis, computational pathology, and multimodal data fusion for precision medicine. He has also contributed to the development of publicly available medical imaging datasets and robust evaluation methods for medical image analysis.

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)

Profiles:

• Google Scholar
• LinkedIn
• ResearchGate

Grants

• LymphoidStructureMiner: AI-based exploration of the immunological contexture of lymphoid structures in translational research (2024)
  Source of Funding: WWTF (Vienna Science and Technology Fund), AI-based medical image analysis
  Principal Investigator

Selected publications

1. Torbati, N. et al. (2026) “A multi-stage auto-context deep learning framework for tissue and nuclei segmentation and classification in H&E-stained histological images of advanced melanoma,” Machine Learning with Applications. Edited by , 25, p. 100933. Available at: https://doi.org/10.1016/j.mlwa.2026.100933.
2. Mahbod, A. et al. (2025) “Evaluating pre-trained convolutional neural networks and foundation models as feature extractors for content-based medical image retrieval,” Engineering Applications of Artificial Intelligence. Edited by , 150, p. 110571. Available at: https://doi.org/10.1016/j.engappai.2025.110571.
3. Mahbod, A. et al. (2024) “NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images,” Scientific Data. Edited by , 11(1). Available at: https://doi.org/10.1038/s41597-024-03117-2.
4. Mahbod, A. et al. (2024) “Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization,” Computational and Structural Biotechnology Journal. Edited by , 23, pp. 669–678. Available at: https://doi.org/10.1016/j.csbj.2023.12.042.
5. 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. Edited by , 9. Available at: https://doi.org/10.3389/fmed.2022.978146.