Dr. Christopher J. Trindade Profile

Dr. Christopher J. Trindade

at FDA/CDRH/Office of Product Evaluation and Quality/OHT7

SPIE Involvement:

Author

Publications (3)

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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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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