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Article ; Online: Deep learning in breast imaging.

Bhowmik, Arka / Eskreis-Winkler, Sarah

BJR open

2022 Volume 4, Issue 1, Page(s) 20210060

Abstract: ... consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL ...

Abstract	Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
Language	English
Publishing date	2022-05-13
Publishing country	England
Document type	Journal Article ; Review
ISSN	2513-9878
ISSN (online)	2513-9878
DOI	10.1259/bjro.20210060
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Article ; Online: Deep learning workflow for the inverse design of molecules with specific optoelectronic properties.

Yoo, Pilsun / Bhowmik, Debsindhu / Mehta, Kshitij / Zhang, Pei / Liu, Frank / Lupo Pasini, Massimiliano / Irle, Stephan

Scientific reports

2023 Volume 13, Issue 1, Page(s) 20031

Abstract: ... burden and accelerate rational molecular design, we here present an iterative deep learning workflow ...

Abstract	The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).
Language	English
Publishing date	2023-11-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-45385-9
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Article: Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.

Roy, Bishwajit / Malviya, Lokesh / Kumar, Radhikesh / Mal, Sandip / Kumar, Amrendra / Bhowmik, Tanmay / Hu, Jong Wan

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 11

Abstract: ... psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long ... signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN ...

Abstract	Stress has an impact, not only on a person's physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.
Language	English
Publishing date	2023-06-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13111936
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Article ; Online: Understanding the intricacy of protein in hydrated deep eutectic solvent: Solvation dynamics, conformational fluctuation dynamics, and stability.

Khan, Tanmoy / Das, Nilimesh / Negi, Kuldeep Singh / Bhowmik, Suman / Sen, Pratik

International journal of biological macromolecules

2023 Volume 253, Issue Pt 5, Page(s) 127100

Abstract: Deep eutectic solvents (DESs) are potential biocatalytic media due to their easy preparation, fine ...

Abstract	Deep eutectic solvents (DESs) are potential biocatalytic media due to their easy preparation, fine-tuneability, biocompatibility, and most importantly, due to their ability to keep protein stable and active. However, there are many unanswered questions and gaps in our knowledge about how proteins behave in these alternate media. Herein, we investigated solvation dynamics, conformational fluctuation dynamics, and stability of human serum albumin (HSA) in 0.5 Acetamide/0.3 Urea/0.2 Sorbitol (0.5Ac/0.3Ur/0.2Sor) DES of varying concentrations to understand the intricacy of protein behaviour in DES. Our result revealed a gradual decrease in the side-chain flexibility and thermal stability of HSA beyond 30 % DES. On the other hand, the associated water dynamics around domain-I of HSA decelerate only marginally with increasing DES content, although viscosity rises considerably. We propose that even though macroscopic solvent properties are altered, a protein feels only an aqueous type of environment in the presence of DES. This is probably the first experimental study to delineate the role of the associated water structure of the enzyme for maintaining its stability inside DES. Although considerable effort is necessary to generalize such claims, it might serve as the basis for understanding why proteins remain stable and active in DES.
MeSH term(s)	Humans ; Solvents/chemistry ; Deep Eutectic Solvents ; Proteins ; Water/chemistry ; Molecular Conformation
Chemical Substances	Solvents ; Deep Eutectic Solvents ; Proteins ; Water (059QF0KO0R)
Language	English
Publishing date	2023-09-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	282732-3
ISSN	1879-0003 ; 0141-8130
ISSN (online)	1879-0003
ISSN	0141-8130
DOI	10.1016/j.ijbiomac.2023.127100
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Article ; Online: Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model.

Bhowmik, Arka / Monga, Natasha / Belen, Kristin / Varela, Keitha / Sevilimedu, Varadan / Thakur, Sunitha B / Martinez, Danny F / Sutton, Elizabeth J / Pinker, Katja / Eskreis-Winkler, Sarah

Investigative radiology

2023 Volume 58, Issue 10, Page(s) 710–719

Abstract: Objectives: The aim of the study is to develop and evaluate the performance of a deep learning (DL ... possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was ...

Abstract	Objectives: The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. Materials and methods: In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. Results: In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. Conclusions: Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
MeSH term(s)	Humans ; Female ; Triage/methods ; Retrospective Studies ; Deep Learning ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2023-04-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80345-5
ISSN	1536-0210 ; 0020-9996
ISSN (online)	1536-0210
ISSN	0020-9996
DOI	10.1097/RLI.0000000000000976
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Article ; Online: Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals

Bishwajit Roy / Lokesh Malviya / Radhikesh Kumar / Sandip Mal / Amrendra Kumar / Tanmay Bhowmik / Jong Wan Hu

Diagnostics, Vol 13, Iss 1936, p

2023 Volume 1936

Abstract	Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.
Keywords	EEG ; DWT ; CNN ; LSTM ; BiLSTM ; GRU ; Medicine (General) ; R5-920
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
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Article ; Online: Deep clustering of protein folding simulations.

Bhowmik, Debsindhu / Gao, Shang / Young, Michael T / Ramanathan, Arvind

BMC bioinformatics

2018 Volume 19, Issue Suppl 18, Page(s) 484

Abstract: Background: We examine the problem of clustering biomolecular simulations using deep learning ...

Abstract	Background: We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. Results: We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Conclusions: Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.
MeSH term(s)	Cluster Analysis ; Molecular Dynamics Simulation ; Protein Folding
Language	English
Publishing date	2018-12-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-018-2507-5
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Article ; Online: Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.

Soni, Mukesh / Khan, Ihtiram Raza / Basir, Sameer / Chadha, Raman / Alguno, Arnold C / Bhowmik, Tapas

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 2455259

Abstract: ... detection method for system failures based on long short-term memory neural networks. In recent years, deep ...

Abstract	Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors.
MeSH term(s)	Deep Learning ; Neural Networks, Computer ; Reproducibility of Results ; Research Design ; Technology
Language	English
Publishing date	2022-06-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/2455259
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Article ; Online: Thermodynamic equivalence of cyclic shear and deep cooling in glass formers.

Bhowmik, Bhanu Prasad / Ilyin, Valery / Procaccia, Itamar

Physical review. E

2020 Volume 102, Issue 1-1, Page(s) 10603

Abstract: ... to achieve such deep quenching were proposed, including vapor deposition on the experimental side and "swap ... in serious difficulties to prepare deeply quenched, well annealed, glassy material. Recently, methods ...

Abstract	The extreme slowing down associated with glass formation in experiments and in simulations results in serious difficulties to prepare deeply quenched, well annealed, glassy material. Recently, methods to achieve such deep quenching were proposed, including vapor deposition on the experimental side and "swap Monte Carlo" and oscillatory shearing on the simulation side. The relation between the resulting glasses under different protocols remains unclear. Here we show that oscillatory shear and swap Monte Carlo result in thermodynamically equivalent glasses sharing the same statistical mechanics and similar mechanical responses under external strain.
Language	English
Publishing date	2020-08-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2844562-4
ISSN	2470-0053 ; 2470-0045
ISSN (online)	2470-0053
ISSN	2470-0045
DOI	10.1103/PhysRevE.102.010603
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Article ; Online: Exome sequencing for perinatal phenotypes: The significance of deep phenotyping.

Aggarwal, Shagun / Vineeth, Venugopal Satidevi / Das Bhowmik, Aneek / Tandon, Ashwani / Kulkarni, Aditya / Narayanan, Dhanya Lakshmi / Bhattacherjee, Amrita / Dalal, Ashwin

Prenatal diagnosis

2019 Volume 40, Issue 2, Page(s) 260–273

Abstract: ... phenotypes following postmortem/postnatal deep phenotyping underwent ES to identify a causative variant ...

Abstract	Objective: To ascertain the performance of exome sequencing (ES) technology for determining the etiological basis of abnormal perinatal phenotypes and to study the impact of comprehensive phenotyping on variant prioritization. Methods: A carefully selected cohort of 32/204 fetuses with abnormal perinatal phenotypes following postmortem/postnatal deep phenotyping underwent ES to identify a causative variant for the fetal phenotype. A retrospective comparative analysis of the prenatal versus postmortem/postnatal phenotype-based variant prioritization was performed with aid of Phenolyzer software. A review of selected literature reports was done to examine the completeness of phenotypic information for cases in those reports and how it impacted the performance of fetal ES. Results: In 18/32 (56%) fetuses, a pathogenic/likely pathogenic variant was identified. This included novel genotype-phenotype associations, expanded prenatal phenotypes of known Mendelian disorders and dual Mendelian diagnoses. The retrospective analysis revealed that the putative diagnostic variant could not be identified on basis of prenatal findings alone in 15/22 (68%) cases, indicating the importance of comprehensive postmortem/postnatal phenotype information. Literature review was supportive of these findings but could not be conclusive due to marked heterogeneity of involved studies. Conclusion: Comprehensive phenotyping is essential for improving diagnostic performance and facilitating identification of novel genotype-phenotype associations in perinatal cohorts undergoing ES.
MeSH term(s)	Autopsy ; Congenital Abnormalities/genetics ; Fetus ; Genetic Association Studies ; Humans ; Phenotype ; Prenatal Diagnosis ; Retrospective Studies ; Whole Exome Sequencing
Language	English
Publishing date	2019-12-05
Publishing country	England
Document type	Journal Article
ZDB-ID	82031-3
ISSN	1097-0223 ; 0197-3851
ISSN (online)	1097-0223
ISSN	0197-3851
DOI	10.1002/pd.5616
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