Mr. M. Ali Vosoughi Profile

M. Ali Vosoughi

at Univ of Rochester

SPIE Involvement:

Author

Area of Expertise:

Machine learning , Kernel machines , Deep learning , Machine vision , Causal and probablistic models

Websites:

Personal Website

Publications (21)

Proceedings Article | 2 April 2024 Paper

Enhancing graph attention neural network performance for marijuana consumption classification through large-scale Augmented Granger Causality (lsAGC) analysis of functional MR images

Ali Vosoughi, Akhil Kasturi, Axel Wismüller

Proceedings Volume 12930, 129300O (2024) https://doi.org/10.1117/12.3008187

KEYWORDS: Brain, Neural networks, Magnetic resonance imaging, Image classification, Functional magnetic resonance imaging, Analytical research, Neuroimaging, Network security, Dynamical systems, Dimension reduction

Read Abstract +

In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state functional Magnetic Resonance Imaging (fMRI). The relationship between marijuana consumption and alterations in brain network connectivity is a recognized fact in scientific literature. This study probes how lsAGC can accurately discern these changes. The technique used integrates dimension reduction with the augmentation of source time-series in a model that predicts time-series, which helps in estimating the directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC uncovers the connection of the inherent dynamic system while considering all other time-series. A dataset of 60 adults with an ADHD diagnosis during childhood, drawn from the Addiction Connectome Preprocessed Initiative (ACPI), was used in the study. The brain connections assessed by lsAGC were utilized as classification attributes. A Graph Attention Neural Network (GAT) was chosen to carry out the classification task, particularly for its ability to harness graph-based data and recognize intricate interactions between brain regions, making it appropriate for fMRI-based brain connectivity data. The performance was analyzed using a five-fold cross-validation system. The average accuracy achieved by the correlation coefficient method was roughly 52.98%, with a 1.65 standard deviation, whereas the lsAGC approach yielded an average accuracy of 61.47%, with a standard deviation of 1.44. A random guess method yielded an average accuracy of about 47.05%, with a standard deviation of around 6.25. The study indicates that lsAGC, when paired with a Graph Attention Neural Network, has the potential to serve as a novel biomarker for pinpointing marijuana users, offering a superior and consistent classification strategy over traditional functional connectivity techniques, including the random guess method. The suggested method enhances the body of knowledge in the field of neuroimaging-based classification and emphasizes the necessity to consider directed causal connections in brain network connectivity analysis when studying marijuana’s effects on the brain.

Proceedings Article | 2 April 2024 Presentation + Paper

Graph attention transformers and large-scale granger causality to classify marijuana consumption from functional MR images

Ali Vosoughi, Akhil Kasturi, Axel Wismüller

Proceedings Volume 12930, 1293014 (2024) https://doi.org/10.1117/12.3008188

KEYWORDS: Brain, Transformers, Magnetic resonance imaging, Image classification, Functional magnetic resonance imaging, Data modeling, Neural networks, Cross validation, Neuroimaging, Network security

Read Abstract +

In this study, we explore the potential of large-scale Granger Causality (lsGC) estimated brain network connectivity as a biomarker for classifying marijuana users from typical controls using resting-state functional Magnetic Resonance Imaging (fMRI). It is well-established in the literature that marijuana use is associated with alterations in brain network connectivity, and we investigate whether lsGC can effectively capture such changes. The lsGC method, a multivariate approach based on dimension reduction and predictive time-series modeling, allows for estimating directed causal relationships among fMRI time series, considering the interdependence of time series within the underlying dynamic system. We employ a dataset consisting of 60 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. Brain connections estimated using lsGC are extracted as features for classification. We utilize a Graph Attention Neural Network (GAT) to accomplish the classification task. The GAT model is specifically chosen for its ability to leverage graph-based data and capture complex interactions between brain regions, making it well-suited for handling fMRI-based brain connectivity data. To assess the performance of our approach, we employ a cross-validation scheme with five-fold cross-validation. The mean accuracy computed for the correlation coefficient method is approximately 53.78%, with a standard deviation of about 4.80, while the mean accuracy for our approach, lsGC, is approximately 64.89%, with a standard deviation of 1.10. The findings suggest that lsGC, in conjunction with a Graph Attention Neural Network, holds promise as a potential biomarker for identifying marijuana users, providing a more effective and reliable classification approach than conventional functional connectivity measures. The proposed methodology offers a valuable contribution to neuroimaging-based classification studies and highlights the importance of considering directed causal relationships in brain network connectivity analysis when investigating the impact of marijuana use on the brain.

Proceedings Article | 28 September 2023 Presentation + Paper

Detecting landmarks in anatomical medical images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12655, 1265508 (2023) https://doi.org/10.1117/12.2678538

KEYWORDS: Deep learning, Transformers, Medical imaging, Biomedical applications, Image segmentation, Chest imaging, Education and training

Read Abstract +

Proceedings Article | 28 September 2023 Presentation + Paper

Leveraging large-scale Granger causality and neural networks to measure the level of consciousness in DoC patients

Ali Vosoughi, Theresa Raiser, Tina Luther, Lina Willacker, Martin Rosenfelder, Akhil Kasturi, Jacobo Diego Sitt, Dragana Manasova, Angela Comanducci, Andreas Bender, Axel Wismüller

Proceedings Volume 12655, 1265507 (2023) https://doi.org/10.1117/12.2677317

KEYWORDS: Magnetic resonance imaging, Consciousness, Medical imaging, Neural networks, Matrices, Functional imaging

Read Abstract +

Proceedings Article | 10 April 2023 Presentation + Paper

Tracking the impact of global iodinated contrast agent shortage on radiology: analysis of CT exam volumes at a major US healthcare

Axel Wismüller, Jerome Avondo, Larry Stockmaster, Akhil Kasturi, Ali Vosoughi

Proceedings Volume 12468, 124680X (2023) https://doi.org/10.1117/12.2654552

KEYWORDS: Magnetic resonance imaging, Computed tomography, Biomedical optics, Medical imaging, Medicine, Radiology, Photonics, Contrast agents, Biomedical applications, Functional imaging

Read Abstract +

Proceedings Article | 10 April 2023 Presentation + Paper

Large-Scale Granger Causality (lsGC) for classification of schizophrenia using functional MRI

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246818 (2023) https://doi.org/10.1117/12.2654504

KEYWORDS: Medical imaging, Matrices, Machine learning, Statistical signal processing, Digital signal processing

Read Abstract +

Proceedings Article | 10 April 2023 Presentation + Paper

Identification of schizophrenia patients using large-scale Extended Granger Causality (lsXGC) in functional MR imaging

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246816 (2023) https://doi.org/10.1117/12.2654499

KEYWORDS: Machine learning, Machine vision, Dimension reduction, Dynamical systems, Medical imaging

Read Abstract +

Proceedings Article | 10 April 2023 Presentation + Paper

Large-scale Augmented Granger Causality (lsAGC) for discovery of causal brain connectivity networks in schizophrenia patients using functional MRI neuroimaging

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246817 (2023) https://doi.org/10.1117/12.2654502

KEYWORDS: Medical imaging, Functional imaging, Matrices, Machine learning, Dimension reduction

Read Abstract +

Proceedings Article | 7 April 2023 Poster + Paper

Cross modal global local representation learning from radiology reports and x-ray chest images

Nathan Hadjiyski, Ali Vosoughi, Axel Wismüller

Proceedings Volume 12465, 1246531 (2023) https://doi.org/10.1117/12.2654520

KEYWORDS: Machine learning, Deep learning, Machine vision, Artificial intelligence

Read Abstract +

Proceedings Article | 7 April 2023 Presentation + Paper

Classification of schizophrenia using large-scale kernelized Granger causality (lsKGC) and functional MR imaging

Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski, Axel Wismüller

Proceedings Volume 12465, 124650A (2023) https://doi.org/10.1117/12.2654506

KEYWORDS: Computer aided detection, Machine vision, Dimension reduction, Digital signal processing, Medical imaging, Matrices

Read Abstract +

Proceedings Article | 27 May 2022 Presentation + Paper

Self-supervised learning in medical imaging: anomaly detection in MRI using autoencoders

Axel Wismüller, M. Ali Vosoughi

Proceedings Volume 12102, 1210206 (2022) https://doi.org/10.1117/12.2622233

KEYWORDS: Magnetic resonance imaging, Medical imaging, Neural networks, Computer vision technology, Machine learning, Machine vision

Read Abstract +

Proceedings Article | 27 May 2022 Presentation + Paper

Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS)

Axel Wismüller, Larry Stockmaster, M. Ali Vosoughi

Proceedings Volume 12101, 121010E (2022) https://doi.org/10.1117/12.2622234

KEYWORDS: Radiology, Artificial intelligence, Computed tomography, Head, Image analysis, Medicine, Medical imaging, Image processing, Scanners, Safety

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Investigation of large-scale extended Granger causality (lsXGC) on synthetic functional MRI data

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12036, 1203609 (2022) https://doi.org/10.1117/12.2613380

KEYWORDS: Functional magnetic resonance imaging, Medical imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference, Networks, Information theory

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12036, 120360A (2022) https://doi.org/10.1117/12.2613389

KEYWORDS: Magnetic resonance imaging, Medical imaging, Brain, Functional imaging, Neural networks, Computer aided diagnosis and therapy, Information theory, Machine learning, Statistical inference, Networks

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC)

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12033, 120330O (2022) https://doi.org/10.1117/12.2611301

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Medical imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Large-scale Kernelized Granger Causality (lsKGC) for inferring topology of directed graphs in brain networks

M. Ali Vosoughi, Axel Wismüller

Proceedings Volume 12036, 1203603 (2022) https://doi.org/10.1117/12.2613459

KEYWORDS: Magnetic resonance imaging, Medical imaging, Functional magnetic resonance imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference, Probability theory, Information theory

Read Abstract +