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Experienced computer vision and machine learning expert with a Ph.D. from the University of Innsbruck, Austria. I specialize in the development and implementation of deep learning algorithms in computer vision applications. Throughout my career, I have worked on several research projects and collaborated with international teams. Passionate about using AI to solve real-world problems and sharing knowledge through educational content and AI consultation sessions.
Experienced computer vision and machine learning expert with a Ph.D. from the University of Innsbruck, Austria. I specialize in the development and implementation of deep learning algorithms in computer vision applications. Throughout my career, I have worked on several research projects and collaborated with international teams. Passionate about using AI to solve real-world problems and sharing knowledge through educational content and AI consultation sessions.
My Github : https://github.com/mrymsadeghi/
My Linkedin: https://www.linkedin.com/in/maryam-sadeghi88/
Projects
Projects
See the projects page for more detailed project list.
Visualizing image embeddings
AMBIA: End-to-end mouse brain analysis pipeline
3D localization of 2D mouse brain slices in the reference brain
Explainability AI
Glomeruli detection
Stable Diffusion
Gui to evaluate the guidance score parameter
Generative adversarial network for brain PET partial volume correction
Car plate detection using Yolov7
Eye tracking in real-time
Publications
Publications
Sadeghi, Maryam, et al. "Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space." Neuroinformatics (2023): 1-16. https://link.springer.com/article/10.1007/s12021-023-09632-8
Sadeghi, M., Neto, P., Ramos-Prats, A., Castaldi, F., Paradiso, E., Mahmoodian, N., ... & Goebel, G. (2022, April). Automatic 2D to 3D localization of histological mouse brain sections in the reference atlas using deep learning. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 718-724). SPIE.
Sadeghi, M., Maldonado, I., Abele, N., Haybaeck, J., Boese, A., Poudel, P., & Friebe, M. (2019, July). Feedback-based self-improving CNN algorithm for breast cancer lymph node metastasis detection in real clinical environment. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 7212-7215). IEEE.
Sadeghi, M., Boese, A., Maldonado, I., Friebe, M., Sauerhering, J., Schlosser, S., ... & Wehberg, K. (2019). Feasibility test of dynamic cooling for detection of small tumors in IR thermographic breast imaging. Current Directions in Biomedical Engineering, 5(1), 397-399.
Sanaat, A., Shooli, H., Böhringer, A. S., Sadeghi, M., Shiri, I., Salimi, Y., ... & Zaidi, H. (2023). A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information. European Journal of Nuclear Medicine and Molecular Imaging, 1-16.
Ramos-Prats, A., Paradiso, E., Castaldi, F., Sadeghi, M., Mir, M. Y., Hörtnagl, H., ... & Ferraguti, F. (2022). VIP-expressing interneurons in the anterior insular cortex contribute to sensory processing to regulate adaptive behavior. Cell Reports, 39(9), 110893.
Mahmoodian, N., Thadesar, M. H., Sadeghi, M. M., Georgiades, M., Pech, M., & Hoeschen, C. (2022). LIVER TUMOR SEGMENTATION USING DEEP LEARNING METHOD: RESLU-NET. Physica Medica: European Journal of Medical Physics, 104, S132.
Mahmoodian, N., Thadesar, H., Sadeghi, M., Georgiades, M., Pech, M., & Hoeschen, C. (2022). Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net. Current Directions in Biomedical Engineering, 8(2), 49-52.
Poudel, P., Illanes, A., Sadeghi, M., & Friebe, M. (2019, July). Patch based texture classification of thyroid ultrasound images using convolutional neural network. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5828-5831). IEEE.
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