PhD project · University Medical Center Groningen
Marie Skłodowska-Curie Fellow · PhD Researcher in Visual Neuroscience
Amir Reza Naderi Yaghouti
I develop deep learning and computer vision methods for medical imaging, currently focused on detecting visual pathway anomalies in glaucoma and rare patient groups.
Joint PhD · Otto-von-Guericke University Magdeburg, Germany & University of Groningen, the Netherlands
- Publications
- 4
- Citations
- 58
- h-index
- 3
- i10-index
- 2
About
I'm Amir Reza Naderi Yaghouti, or Amir Naderi for short, a Marie Skłodowska-Curie Doctoral Candidate in the EGRET-AAA network, pursuing a joint PhD in visual neuroscience at Otto-von-Guericke University Magdeburg and the University of Groningen. In Magdeburg I work at the University Clinic for Ophthalmology under the supervision of Prof. Michael B. Hoffmann and Dr. Khaldoon O. Al-Nosairy; in Groningen I work with Prof. Nomdo M. Jansonius.
My research uses deep learning and computer vision to build intelligent, data-driven diagnostic tools. Before glaucoma, I worked on machine learning for liver disease: grading nonalcoholic fatty liver disease from ultrasound images and detecting non-alcoholic steatohepatitis from clinical and blood parameters. I was born and raised in Tehran, where I earned my BSc and MSc in Biomedical Engineering.
Research interests
- Medical image analysis
- Glaucoma & optic nerve head
- Visual pathway anomalies
- Deep learning
- Computer vision
- Explainable & trustworthy AI
- Biomarker discovery
- Ultrasound imaging
News
Oral 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)
Poster presentation at EVER 2026, the European Association for Vision and Eye Research conference, in Florence, Italy.
Poster presentation at ECVP 2025, the European Conference on Visual Perception, in Mainz, Germany.
New 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)
New 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)
Started 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)
Show earlier news
New 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)
Publications
Full list on Google ScholarUnder review
- Discrimination of benign and malignant ovarian tumors from routine laboratory biomarkers using a tabular foundation model
Sci. Rep.Scientific Reports
2025
- Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images
J. Biomed. Phys. Eng.Journal of Biomedical Physics and EngineeringCited by 5
PaperScholarCite
@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} } - Artificial intelligence for ovarian cancer detection with medical images: a review of the last decade (2013–2023)
Arch. Comput. Methods Eng.Archives of Computational Methods in Engineering 32 (7), 4093-4124Cited by 13
PaperScholarCite
@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} }
2024
- Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters
Sci. Rep.Scientific Reports 14, 2442Cited by 37
PaperDOIScholarCite
@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} }
2021
- Automatic classification of Non-alcoholic fatty liver using texture features from ultrasound images
Tehran Univ. Med. J.Tehran University Medical Journal 79 (1), 10–17Cited by 3
PaperScholarCite
@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} }
Research
My doctoral research is part of EGRET-AAA, a Marie Skłodowska-Curie Doctoral Network for advanced glaucoma research, and is shared between Magdeburg and Groningen.
PhD project · Otto-von-Guericke University Magdeburg
Deep learning segmentation of anatomical MRI quantifies optic chiasm degeneration in glaucoma
With Prof. Michael B. Hoffmann and Dr. Khaldoon O. Al-Nosairy
Earlier research
-
Machine learning for liver disease and ovarian cancer
In collaboration with Dr. Ahmad Shalbaf, Shahid Beheshti University
Deep learning and radiomics pipelines for grading fatty liver disease on ultrasound, early detection of non-alcoholic steatohepatitis from clinical and blood data, and a review of AI for ovarian cancer detection in medical images. Published in Scientific Reports, Archives of Computational Methods in Engineering, the Journal of Biomedical Physics and Engineering and the Tehran University Medical Journal.
Talks & posters
- 2026Oral
CLINICCAI 2026, MICCAI 2026 Clinical Day · Strasbourg, FranceGlaucoma Biomarkers Beyond the Optic Disc: Vascular Signal Quantified via Anatomical Masking of a Retinal Foundation Model
- 2026Poster
EVER 2026, European Association for Vision and Eye Research · Florence, Italy
- 2025Poster
ECVP 2025, European Conference on Visual Perception · Mainz, Germany
Education
-


Doctor of Philosophy (PhD), Visual Neuroscience
Otto-von-Guericke University Magdeburg, Germany
University of Groningen · University Medical Center Groningen, the Netherlands
Marie Skłodowska-Curie Doctoral Fellow, EGRET-AAA
-

Master of Science (MSc), Biomedical Engineering
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
GPA 18.43 / 20 (3.77 / 4.0)
-

Bachelor of Science (BSc), Biomedical Engineering
Islamic Azad University, Science and Research Branch, Tehran
Thesis Automated breast lesion segmentation and classification in ultrasound imaging via texture analysis
GPA 18.09 / 20 (final year)
Honors & awards
- 2024
Marie Skłodowska-Curie Doctoral Fellowship, EGRET-AAA, EU Horizon Europe
- 2021
Highest possible grade for the Master's thesis
- 2021
Top student of the Master's program, with the highest grades in most courses
Skills & languages
- Methods
- Deep learning (CNNs, transfer learning, segmentation) · Medical image analysis (fundus, MRI, ultrasound) · Radiomics & texture analysis · Explainable AI · Machine learning on clinical data · Signal processing · Natural language processing
- Programming
- Python (PyTorch, TensorFlow, MONAI, scikit-learn, OpenCV) · MATLAB · R
- Tools
- LaTeX · Git · SPSS · Minitab · RapidMiner · Microsoft Office
- Languages
- Persian (native) · English (C1) · German (A1)
Contact
I'm happy to talk about research collaborations, medical imaging, and AI for ophthalmology.
Funded by the European Union. This project has received funding from the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101072435 (EGRET-AAA). Views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union or the granting authority.