AI in Medicine · Marie Skłodowska-Curie Fellow
I develop machine learning and deep learning methods for medical data, from MRI, retinal and ultrasound images to clinical and laboratory records, to make diagnosis earlier and more reliable.
I'm Amir Reza Naderi Yaghouti, or Amir Naderi for short: a researcher in AI in medicine and a Marie Skłodowska-Curie Fellow in the EGRET-AAA network, doing a joint PhD at Otto-von-Guericke University Magdeburg and the University of Groningen.
My research applies AI across medicine: imaging, clinical records and laboratory data. In my PhD I use deep learning, foundation models and explainable AI to find signs of glaucoma in retinal images and brain MRI.
Before that, I built models for liver disease and ovarian cancer from ultrasound images, blood tests and routine lab biomarkers. I was born and raised in Tehran, where I studied Biomedical Engineering.
Ophthalmology · PhD
Masking anatomical regions in fundus images shows which parts a retinal foundation model relies on to detect glaucoma, including vascular signal outside the optic disc.
Neuroimaging · PhD
Deep learning segmentation of anatomical MRI measures how glaucoma affects the optic chiasm, where the visual pathways cross in the brain.
Hepatology
Grading fatty liver on B-mode ultrasound with pre-trained CNNs, and detecting non-alcoholic steatohepatitis early from clinical and blood parameters.
Oncology
A review of a decade of AI for ovarian cancer imaging, and a tabular foundation model that separates benign from malignant tumors using routine lab biomarkers.
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Journal of Biomedical Physics and Engineering
@article{naderiyaghouti2025classification,
title = {{Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images}},
author = {Naderi Yaghouti, A. R. and Shalbaf, A.},
journal = {Journal of Biomedical Physics and Engineering},
year = {2025}
}Archives of Computational Methods in Engineering 32 (7), 4093-4124
@article{naderiyaghouti2025artificial,
title = {{Artificial intelligence for ovarian cancer detection with medical images: a review of the last decade (2013–2023)}},
author = {Naderi Yaghouti, A. R. and Shalbaf, A. and Alizadehsani, R. and Tan, R. S. and Vijayananthan, A. and others},
journal = {Archives of Computational Methods in Engineering},
volume = {32},
number = {7},
pages = {4093--4124},
year = {2025},
doi = {10.1007/s11831-025-10268-x}
}Scientific Reports 14, 2442
@article{naderiyaghouti2024machine,
title = {{Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters}},
author = {Naderi Yaghouti, A. R. and Zamanian, H. and Shalbaf, A.},
journal = {Scientific Reports},
volume = {14},
pages = {2442},
year = {2024},
doi = {10.1038/s41598-024-51741-0}
}Tehran University Medical Journal 79 (1), 10–17
@article{naderiyaghouti2021automatic,
title = {{Automatic classification of Non-alcoholic fatty liver using texture features from ultrasound images}},
author = {Naderi Yaghouti, A. R. and Shalbaf, A. and Maghsoudi, A.},
journal = {Tehran University Medical Journal},
volume = {79},
number = {1},
pages = {10--17},
year = {2021}
}TalkOral presentation at CLINICCAI 2026 (MICCAI 2026 Clinical Day) in Strasbourg, France: “Glaucoma Biomarkers Beyond the Optic Disc: Vascular Signal Quantified via Anatomical Masking of a Retinal Foundation Model”. Read more (opens in new tab)
PosterPoster presentation at EVER 2026, the European Association for Vision and Eye Research conference, in Florence, Italy.
PosterPoster presentation at ECVP 2025, the European Conference on Visual Perception, in Mainz, Germany.
PaperNew review in Archives of Computational Methods in Engineering: artificial intelligence for ovarian cancer detection with medical images over the last decade (2013–2023). Read more (opens in new tab)
PaperNew paper in the Journal of Biomedical Physics and Engineering: classifying nonalcoholic fatty liver grades with pre-trained CNNs and a random forest on B-mode ultrasound. Read more (opens in new tab)
MilestoneStarted a joint PhD in Visual Neuroscience at Otto-von-Guericke University Magdeburg and the University of Groningen as a Marie Skłodowska-Curie Doctoral Candidate (EGRET-AAA, Project 9), working on deep learning for visual pathway anomalies in glaucoma. Read more (opens in new tab)
PaperNew paper in Scientific Reports: machine learning approaches for early detection of non-alcoholic steatohepatitis (NASH) from clinical and blood parameters. Read more (opens in new tab)
MICCAI Clinical Day · Strasbourg, France
Glaucoma Biomarkers Beyond the Optic Disc: Vascular Signal Quantified via Anatomical Masking of a Retinal Foundation ModelEuropean Association for Vision and Eye Research · Florence, Italy
European Conference on Visual Perception · Mainz, Germany
Otto-von-Guericke University Magdeburg · University of Groningen / UMC Groningen
Marie Skłodowska-Curie Doctoral Fellow, EGRET-AAA
Islamic Azad University, Science and Research Branch, Tehran
Thesis: Transfer learning with deep convolutional neural networks for classification of liver steatosis: a comparison of radiomics and convolutional features
Islamic Azad University, Science and Research Branch, Tehran
Thesis: Automated breast lesion segmentation and classification in ultrasound imaging via texture analysis
Marie Skłodowska-Curie Doctoral Fellowship, EU Horizon Europe
Highest possible grade for the Master's thesis
Top student of the Master's program, with the highest grades in most courses
Contact
Open to research collaborations in medical AI, from imaging to clinical data.