Mr. Corey McMillan Profile

Corey McMillan

at Univ of Pennsylvania

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Publications (2)

Proceedings Article | 3 April 2024 Poster + Paper

Video and synthetic MRI pre-training of 3D vision architectures for neuroimage analysis

Nikhil Dhinagar, Amit Singh, Saket Ozarkar, Ketaki Buwa, Sophia Thomopoulos, Conor Owens-Walton, Emily Laltoo, Yao-Liang Chen, Philip Cook, Corey McMillan, Chih-Chien Tsai, J-J Wang, Yih-Ru Wu, Paul Thompson

Proceedings Volume 12927, 129272B (2024) https://doi.org/10.1117/12.3008837

KEYWORDS: Magnetic resonance imaging, Brain, Neuroimaging, Video, Machine learning, Artificial intelligence

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Proceedings Article | 10 December 2021 Paper

3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

Nikhil Dhinagar, Sophia Thomopoulos, Conor Owens-Walton, Dimitris Stripelis, Jose Luis Ambite, Greg Ver Steeg, Daniel Weintraub, Philip Cook, Corey McMillan, Paul Thompson

Proceedings Volume 12088, 120880W (2021) https://doi.org/10.1117/12.2606297

KEYWORDS: Magnetic resonance imaging, Brain, Neuroimaging, Alzheimer's disease, Convolutional neural networks, Computer vision technology, Digital image processing, Parkinson's disease

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Parkinson’s disease (PD) and Alzheimer’s disease (AD) are progressive neurodegenerative disorders that affect millions of people worldwide. In this work, we propose a deep learning approach to classify these diseases based on 3D T1- weighted brain MRI. We analyzed several datasets including the Parkinson's Progression Markers Initiative (PPMI), an independent dataset from the University of Pennsylvania School of Medicine (UPenn), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Open Access Series of Imaging Studies (OASIS) dataset. PPMI and ADNI were partitioned to train (70%), validate (20%), and test (10%) a 3D convolutional neural network (CNN) for PD and AD classification. The UPenn and OASIS datasets were used as independent test sets to evaluate the model performance during inference. We also implemented a random forest classifier as a baseline model by extracting key radiomics features from the same T1-weighted MRI scans. The proposed 3D CNN model was trained from scratch for the classification tasks. For AD classification, the 3D CNN model achieved an ROC-AUC of 0.878 on the ADNI test set and an average ROC-AUC of 0.789 on the OASIS dataset. For PD classification, the proposed 3D CNN model achieved an ROC-AUC of 0.667 on the PPMI test set and an average ROC-AUC of 0.743 on the UPenn dataset. We also found that model performance was largely maintained when using only 25% of the training dataset. The 3D CNN outperformed the random forest classifier for both the PD and AD tasks. The 3D CNN also generalized better on unseen MRI data from different imaging centers. Our results show that the proposed 3D CNN model was less prone to overfitting for AD than for PD classification. This approach shows promise for screening of PD and AD patients using only T1-weighted brain MRI, which is relatively widely available. This model with additional validation could also be used to help differentiate between challenging cases of AD and PD when they present with similarly subtle motor and non-motor symptoms.

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