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Monibor Rahman

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Prof. Saeid Eslamian

Isfahan University of Technology

Norazian Mohamed Noor (PhD)

Universiti Malaysia Perlis (UniMAP)

Kansas State University

Bangladesh University of Engineering & Technology

Mikail F Lumentut

National Taiwan University

mustafa zeki yilmazoglu

Gazi University

Shanxi University

Florentin Smarandache

University of New Mexico

Yalova University

Ataturk University

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Papers by Monibor Rahman

Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

arXiv (Cornell University), Apr 24, 2024

Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (... more Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a LSTM-CNN binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

SRF Cavity Fault Prediction Using Deep Learning at Jefferson Lab

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), May 1, 2024

In this study, we present a deep learning-based pipeline for predicting superconducting radio-fre... more In this study, we present a deep learning-based pipeline for predicting superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. We leverage pre-fault RF signals from C100-type cavities and employ deep learning to predict faults in advance of their onset. We train a binary classifier model to distinguish between stable and impending fault signals. Test results show accuracies exceeding 99% for distinguishing between normal signals and prefault signals from a class of more slowly developing fault types, such as microphonics. We describe results from a proof-of-principle demonstration on a realistic, imbalanced data set and report performance metrics. Encouraging results suggest that future SRF systems could leverage this framework and implement measures to mitigate the onset in more slowly developing fault types.

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

ICALEPCS2021, 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Shanghai, China, 14-22 October 2021

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recircu... more The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linear accelerator (linac) that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper, we report initial results of predicting a fault onset using only data prior to the failure event. A dataset was constructed using time-series data immediately before a fault ("unstable") and 1.5 seconds prior to a fault ("stable") gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behaviour of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach. Based on encouraging initial results, we outline a path forward to leverage deep learning on streaming data for fault type prediction.

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Mar 1, 2022

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recircu... more The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linear accelerator (linac) that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper, we report initial results of predicting a fault onset using only data prior to the failure event. A dataset was constructed using time-series data immediately before a fault ("unstable") and 1.5 seconds prior to a fault ("stable") gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behaviour of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach. Based on encouraging initial results, we outline a path forward to leverage deep learning on streaming data for fault type prediction.

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Aug 1, 2022

Data-driven prediction of future faults is a major research area for many industrial applications... more Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second step of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. Our fault prediction model shows poor model performance on fast-developing fault types.

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

2023 International Joint Conference on Neural Networks (IJCNN)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

The journal of machine learning for biomedical imaging, Aug 1, 2022

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging b... more Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

SRF Cavity Fault Classification and Prediction at Jefferson Lab

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Nov 1, 2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

Frontiers in Artificial Intelligence

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting ra... more This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This...

Brain Tumor Segmentation Using UNet-Context Encoding Network

Lecture Notes in Computer Science, 2022

Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters

Lecture Notes in Computer Science, 2022

Computational Modeling Of Trust Factors in Recognition of Warfighting Ships

Computational Modeling of Trust Factors in Recognition of Warfighting Ships S. Lamczyk, L. Vidyar... more Computational Modeling of Trust Factors in Recognition of Warfighting Ships S. Lamczyk, L. Vidyaratne, M. Rahman, and K. M. Iftekharuddin Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529 ABSTRACT As machine-learning (ML) algorithms continue to handle increasingly intricate problems, these methods are often required to process incomplete, imprecise, and ambiguous sensor data for decision making. The capabilities of these ML algorithms must be developed further in order to meet the increasingly complex demand associated with the deployment and integration of decision-making capabilities of intelligent systems into modern society. Furthermore, the variability in the performance of modern ML methods in dynamic environments leads to ambiguity of trust in the decisions made by such algorithms. Consequently, we propose a novel computational modeling for trust quantification to analyze the performance and improve the trustworthiness o...

Brain Tumor Classification Using 3D Convolutional Neural Network

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

In this paper, we propose a deep learning-based method for brain tumor classification. It is comp... more In this paper, we propose a deep learning-based method for brain tumor classification. It is composed of two parts. The first part is brain tumor segmentation on the multimodal magnetic resonance image (mMRI), and the second part performs tumor classification using tumor segmentation results. A 3D deep neural network is implemented to differentiate tumor from normal tissues, subsequentially, a second 3D deep neural network is developed for tumor classification. We evaluate the proposed method using pateint dataset from Computational Precision Medicine: Radiology-Pathology Challenge (CPM: Rad-Path) on Brain Tumor Classification 2019. The result offers 0.749 for dice score and 0.764 for F1 score for validation data, while 0.596 for dice score and of 0.603 for F1 score for testing data, respectively. Our team was ranked second in the CPM:Rad-Path challenge on Brain Tumor Classification 2019 based on overall testing performance.

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

Physical Review Accelerators and Beams, 2020

We report on the development of machine learning models for classifying C100 superconducting radi... more We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of the eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time consuming. By leveraging machine learning, near real-time-rather than post-mortem-identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques, and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Scientific Reports, 2020

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation an... more A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Com...

Using AI for Management of Field Emission in SRF Linacs

U.S. Department of Energy Office of Scientific and Technical Information - OSTI OAI, Mar 14, 2022

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results

ArXiv, 2021

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of med... more Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantifica...

Unsupervised Multivariate Time Series Clustering

Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction

One of the most challenging problems encountered in deep learning-based brain tumor segmentation ... more One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of natural scenes, with particular improvements in small object segmentation due to improved representative feature learning. Accordingly, we propose a novel, efficient 3DCNN based deep learning framework with context encoding for semantic brain tumor segmentation using multimodal ma...

Brain Tumor Classification Using 3D Convolutional Neural Network

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

In this paper, we propose a deep learning-based method for brain tumor classification. It is comp... more In this paper, we propose a deep learning-based method for brain tumor classification. It is composed of two parts. The first part is brain tumor segmentation on the multimodal magnetic resonance image (mMRI), and the second part performs tumor classification using tumor segmentation results. A 3D deep neural network is implemented to differentiate tumor from normal tissues, subsequentially, a second 3D deep neural network is developed for tumor classification. We evaluate the proposed method using pateint dataset from Computational Precision Medicine: Radiology-Pathology Challenge (CPM: Rad-Path) on Brain Tumor Classification 2019. The result offers 0.749 for dice score and 0.764 for F1 score for validation data, while 0.596 for dice score and of 0.603 for F1 score for testing data, respectively. Our team was ranked second in the CPM:Rad-Path challenge on Brain Tumor Classification 2019 based on overall testing performance.

Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

arXiv (Cornell University), Apr 24, 2024

Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (... more Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a LSTM-CNN binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

SRF Cavity Fault Prediction Using Deep Learning at Jefferson Lab

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), May 1, 2024

In this study, we present a deep learning-based pipeline for predicting superconducting radio-fre... more In this study, we present a deep learning-based pipeline for predicting superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. We leverage pre-fault RF signals from C100-type cavities and employ deep learning to predict faults in advance of their onset. We train a binary classifier model to distinguish between stable and impending fault signals. Test results show accuracies exceeding 99% for distinguishing between normal signals and prefault signals from a class of more slowly developing fault types, such as microphonics. We describe results from a proof-of-principle demonstration on a realistic, imbalanced data set and report performance metrics. Encouraging results suggest that future SRF systems could leverage this framework and implement measures to mitigate the onset in more slowly developing fault types.

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

ICALEPCS2021, 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Shanghai, China, 14-22 October 2021

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recircu... more The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linear accelerator (linac) that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper, we report initial results of predicting a fault onset using only data prior to the failure event. A dataset was constructed using time-series data immediately before a fault ("unstable") and 1.5 seconds prior to a fault ("stable") gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behaviour of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach. Based on encouraging initial results, we outline a path forward to leverage deep learning on streaming data for fault type prediction.

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Mar 1, 2022

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recircu... more The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linear accelerator (linac) that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper, we report initial results of predicting a fault onset using only data prior to the failure event. A dataset was constructed using time-series data immediately before a fault ("unstable") and 1.5 seconds prior to a fault ("stable") gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behaviour of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach. Based on encouraging initial results, we outline a path forward to leverage deep learning on streaming data for fault type prediction.

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Aug 1, 2022

Data-driven prediction of future faults is a major research area for many industrial applications... more Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second step of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. Our fault prediction model shows poor model performance on fast-developing fault types.

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

2023 International Joint Conference on Neural Networks (IJCNN)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

The journal of machine learning for biomedical imaging, Aug 1, 2022

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging b... more Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

SRF Cavity Fault Classification and Prediction at Jefferson Lab

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Nov 1, 2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

Frontiers in Artificial Intelligence

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting ra... more This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This...

Brain Tumor Segmentation Using UNet-Context Encoding Network

Lecture Notes in Computer Science, 2022

Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters

Lecture Notes in Computer Science, 2022

Computational Modeling Of Trust Factors in Recognition of Warfighting Ships

Computational Modeling of Trust Factors in Recognition of Warfighting Ships S. Lamczyk, L. Vidyar... more Computational Modeling of Trust Factors in Recognition of Warfighting Ships S. Lamczyk, L. Vidyaratne, M. Rahman, and K. M. Iftekharuddin Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529 ABSTRACT As machine-learning (ML) algorithms continue to handle increasingly intricate problems, these methods are often required to process incomplete, imprecise, and ambiguous sensor data for decision making. The capabilities of these ML algorithms must be developed further in order to meet the increasingly complex demand associated with the deployment and integration of decision-making capabilities of intelligent systems into modern society. Furthermore, the variability in the performance of modern ML methods in dynamic environments leads to ambiguity of trust in the decisions made by such algorithms. Consequently, we propose a novel computational modeling for trust quantification to analyze the performance and improve the trustworthiness o...

Brain Tumor Classification Using 3D Convolutional Neural Network

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

In this paper, we propose a deep learning-based method for brain tumor classification. It is comp... more In this paper, we propose a deep learning-based method for brain tumor classification. It is composed of two parts. The first part is brain tumor segmentation on the multimodal magnetic resonance image (mMRI), and the second part performs tumor classification using tumor segmentation results. A 3D deep neural network is implemented to differentiate tumor from normal tissues, subsequentially, a second 3D deep neural network is developed for tumor classification. We evaluate the proposed method using pateint dataset from Computational Precision Medicine: Radiology-Pathology Challenge (CPM: Rad-Path) on Brain Tumor Classification 2019. The result offers 0.749 for dice score and 0.764 for F1 score for validation data, while 0.596 for dice score and of 0.603 for F1 score for testing data, respectively. Our team was ranked second in the CPM:Rad-Path challenge on Brain Tumor Classification 2019 based on overall testing performance.

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

Physical Review Accelerators and Beams, 2020

We report on the development of machine learning models for classifying C100 superconducting radi... more We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of the eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time consuming. By leveraging machine learning, near real-time-rather than post-mortem-identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques, and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Scientific Reports, 2020

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation an... more A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Com...

Using AI for Management of Field Emission in SRF Linacs

U.S. Department of Energy Office of Scientific and Technical Information - OSTI OAI, Mar 14, 2022

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results

ArXiv, 2021

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of med... more Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantifica...

Unsupervised Multivariate Time Series Clustering

Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction

One of the most challenging problems encountered in deep learning-based brain tumor segmentation ... more One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of natural scenes, with particular improvements in small object segmentation due to improved representative feature learning. Accordingly, we propose a novel, efficient 3DCNN based deep learning framework with context encoding for semantic brain tumor segmentation using multimodal ma...

Brain Tumor Classification Using 3D Convolutional Neural Network

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

In this paper, we propose a deep learning-based method for brain tumor classification. It is comp... more In this paper, we propose a deep learning-based method for brain tumor classification. It is composed of two parts. The first part is brain tumor segmentation on the multimodal magnetic resonance image (mMRI), and the second part performs tumor classification using tumor segmentation results. A 3D deep neural network is implemented to differentiate tumor from normal tissues, subsequentially, a second 3D deep neural network is developed for tumor classification. We evaluate the proposed method using pateint dataset from Computational Precision Medicine: Radiology-Pathology Challenge (CPM: Rad-Path) on Brain Tumor Classification 2019. The result offers 0.749 for dice score and 0.764 for F1 score for validation data, while 0.596 for dice score and of 0.603 for F1 score for testing data, respectively. Our team was ranked second in the CPM:Rad-Path challenge on Brain Tumor Classification 2019 based on overall testing performance.