Dr. Seyed Mostafa M. Kahaki Profile

Dr. Seyed Mostafa M. Kahaki

at US Food and Drug Administration

SPIE Involvement:

Author

Area of Expertise:

Artificial Intelligence , Machine Learning , Digital Pathology. , Medical Image Analysis

Seyed Kahaki is a staff fellow in the Center for Devices and Radiological Health at the FDA. He conducts research and supports regulatory review of medical devices with his expertise in machine learning, medical image analysis and digital pathology. Previously he served as research fellow at Harvard Medical School and Northeastern University focusing on machine learning and digital pathology.

Proceedings Article | 3 April 2024 Presentation + Paper

Deep learning-based ROI detection of AEH and EC on histopathology WSIs for predicting hormonal treatment response

Seyed Kahaki, Ian Hagemann, Kenny Cha, Christopher Trindade, Nicholas Petrick, Nicolas Kostelecky, Lindsay Borden, Doaa Atwi, Kar-Ming Fung, Weijie Chen

Proceedings Volume 12933, 129330S (2024) https://doi.org/10.1117/12.3008276

KEYWORDS: Deep learning, Performance modeling, Machine learning, Pathology, Data modeling, Cancer, Therapeutics, Oncology, Medicine

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Proceedings Article | 3 April 2024 Paper

Assessment of machine learning algorithms for TILs scoring using whole slide images: comparison with pathologists

Arian Arab, Victor Garcia, Seyed Mostafa Kahaki, Nicholas Petrick, Brandon Gallas, Weijie Chen

Proceedings Volume 12933, 1293315 (2024) https://doi.org/10.1117/12.3008518

KEYWORDS: Calibration, Tumors, Image segmentation, Algorithm development, Machine learning, Detection and tracking algorithms, Cancer detection, Breast cancer

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SPIE Journal Paper | 15 February 2024

End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer

Seyed Mostafa M. Kahaki, Ian Hagemann, Kenny Cha, Christopher Trindade, Nicholas Petrick, Nicolas Kostelecky, Lindsay Borden, Doaa Atwi, Kar-Ming Fung, Weijie Chen

JMI, Vol. 11, Issue 01, 017502, (February 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.017502

KEYWORDS: Education and training, Deep learning, Data modeling, Performance modeling, Feature extraction, Machine learning, Radiomics, RGB color model, Tumor growth modeling, Statistical modeling

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Proceedings Article | 7 April 2023 Presentation + Paper

Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study

Seyed Kahaki, Ian Hagemann, Kenny Cha, Christopher Trindade, Nicholas Petrick, Nicolas Kostelecky, Weijie Chen

Proceedings Volume 12471, 124710T (2023) https://doi.org/10.1117/12.2652912

KEYWORDS: Machine learning, Tumor growth modeling, Deep learning, RGB color model, Performance modeling, Pathology, Statistical modeling, Feature extraction

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Proceedings Article | 15 February 2021 Presentation + Paper

Correction of topological errors in automated traces of neurites

Seyed Mostafa Mousavi Kahaki, Hang Deng, Armen Stepanyants

Proceedings Volume 11596, 115961Q (2021) https://doi.org/10.1117/12.2581247

KEYWORDS: Neurons, Neural networks, Algorithm development, Optical microscopy, Neuroimaging, Machine learning, Gold, Brain, 3D image processing

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Proceedings Article | 15 March 2019 Presentation + Paper

Artificial neural network filters for enhancing 3D optical microscopy images of neurites

Shih-Luen Wang, Seyed M. Kahaki, Armen Stepanyants

Proceedings Volume 10949, 109490G (2019) https://doi.org/10.1117/12.2512989

KEYWORDS: Image filtering, Neurons, 3D image processing, Image quality, Optical microscopy, Artificial neural networks, Image enhancement, Machine learning, Image segmentation

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Proceedings Article | 15 March 2019 Presentation + Paper

Accurate registration of in vivo time-lapse images

Seyed M. M. Kahaki, Shih-Luen Wang, Armen Stepanyants

Proceedings Volume 10949, 109491D (2019) https://doi.org/10.1117/12.2512257

KEYWORDS: Image registration, In vivo imaging, Axons, 3D image processing, Neuroimaging, Brain, Algorithm development, Automatic alignment

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