Kayvan Najarian

Professor of Comp Med and Bioinf, Emergency Med, and Electrical and Comp Engineering

Research Expertise

biomedical inforamtics
bioinformatics
singal processing
image processing
machine learning
Artificial Intelligence
Molecular Biology
Information Systems
Computer Science Applications
Pharmacology
Genetics
Molecular Medicine
Health Informatics
Radiology, Nuclear Medicine and imaging
Surgery
Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Biomedical Engineering
Theoretical Computer Science
Signal Processing
Health Policy
Health Information Management
Electrical and Electronic Engineering
Applied Mathematics
Modeling and Simulation
Gastroenterology
Hardware and Architecture
Software
Anesthesiology and Pain Medicine
Computer Networks and Communications
Media Technology
Cancer Research
Oncology
Cell Biology
Biochemistry
Biotechnology
Metals and Alloys
Surfaces, Coatings and Films
Condensed Matter Physics
Instrumentation
Electronic, Optical and Magnetic Materials
Physiology (medical)
Physiology
Biophysics
Critical Care and Intensive Care Medicine
Clinical Biochemistry
Library and Information Sciences
Control and Systems Engineering
Orthodontics

Publications

Big Data Analytics in Healthcare

BioMed Research International / Jan 01, 2015

Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big Data Analytics in Healthcare. BioMed Research International, 2015, 1–16. https://doi.org/10.1155/2015/370194

Signals and Biomedical Signal Processing

Biomedical Signal and Image Processing / Apr 19, 2016

Najarian, K., & Splinter, R. (2016). Signals and Biomedical Signal Processing. Biomedical Signal and Image Processing, 3–14. https://doi.org/10.1201/b11978-3

Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine / Jun 01, 2012

Vyas, N., Farringdon, J., Andre, D., & Stivoric, J. (Ivo). (2012). Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure. AI Magazine, 33(2), 55–66. Portico. https://doi.org/10.1609/aimag.v33i2.2408

Melanoma detection by analysis of clinical images using convolutional neural network

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Aug 01, 2016

Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., & Najarian, K. (2016). Melanoma detection by analysis of clinical images using convolutional neural network. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2016.7590963

Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

Briefings in Bioinformatics / Jan 17, 2020

Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska-Coleska, Z., & Najarian, K. (2020). Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Briefings in Bioinformatics, 22(1), 247–269. https://doi.org/10.1093/bib/bbz157

Skin lesion segmentation in clinical images using deep learning

2016 23rd International Conference on Pattern Recognition (ICPR) / Dec 01, 2016

Jafari, M. H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S. M. R., Ward, K., & Najarian, K. (2016). Skin lesion segmentation in clinical images using deep learning. 2016 23rd International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr.2016.7899656

Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2018

Akbari, M., Mohrekesh, M., Nasr-Esfahani, E., Soroushmehr, S. M. R., Karimi, N., Samavi, S., & Najarian, K. (2018). Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2018.8512197

ReDMark: Framework for residual diffusion watermarking based on deep networks

Expert Systems with Applications / May 01, 2020

Ahmadi, M., Norouzi, A., Karimi, N., Samavi, S., & Emami, A. (2020). ReDMark: Framework for residual diffusion watermarking based on deep networks. Expert Systems with Applications, 146, 113157. https://doi.org/10.1016/j.eswa.2019.113157

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Pharmacogenomics / May 01, 2018

Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D. (2018). Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics, 19(7), 629–650. https://doi.org/10.2217/pgs-2018-0008

Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

International Journal of Computer Assisted Radiology and Surgery / Mar 24, 2017

Jafari, M. H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S. M. R., Samavi, S., & Najarian, K. (2017). Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma. International Journal of Computer Assisted Radiology and Surgery, 12(6), 1021–1030. https://doi.org/10.1007/s11548-017-1567-8

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

ACM Computing Surveys / Aug 25, 2020

Wood, A., Najarian, K., & Kahrobaei, D. (2020). Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. ACM Computing Surveys, 53(4), 1–35. https://doi.org/10.1145/3394658

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2019

Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., & Reza Soroushmehr, S. M. (2019). Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2019.8856981

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

IEEE Reviews in Biomedical Engineering / Jan 01, 2017

Ansari, S., Farzaneh, N., Duda, M., Horan, K., Andersson, H. B., Goldberger, Z. D., Nallamothu, B. K., & Najarian, K. (2017). A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Reviews in Biomedical Engineering, 10, 264–298. https://doi.org/10.1109/rbme.2017.2757953

Maximizing strength of digital watermarks using neural networks

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

Davis, K. J., & Najarian, K. (n.d.). Maximizing strength of digital watermarks using neural networks. IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222). https://doi.org/10.1109/ijcnn.2001.938836

Vessel extraction in X-ray angiograms using deep learning

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Aug 01, 2016

Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Ward, K., Jafari, M. H., Felfeliyan, B., Nallamothu, B., & Najarian, K. (2016). Vessel extraction in X-ray angiograms using deep learning. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2016.7590784

Segmentation of vessels in angiograms using convolutional neural networks

Biomedical Signal Processing and Control / Feb 01, 2018

Nasr-Esfahani, E., Karimi, N., Jafari, M. H., Soroushmehr, S. M. R., Samavi, S., Nallamothu, B. K., & Najarian, K. (2018). Segmentation of vessels in angiograms using convolutional neural networks. Biomedical Signal Processing and Control, 40, 240–251. https://doi.org/10.1016/j.bspc.2017.09.012

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

The Scientific World Journal / Jan 01, 2013

Belle, A., Kon, M. A., & Najarian, K. (2013). Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey. The Scientific World Journal, 2013, 1–8. https://doi.org/10.1155/2013/769639

A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

The Scientific World Journal / Jan 01, 2013

Luo, Y., Hargraves, R. H., Belle, A., Bai, O., Qi, X., Ward, K. R., Pfaffenberger, M. P., & Najarian, K. (2013). A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis. The Scientific World Journal, 2013, 1–10. https://doi.org/10.1155/2013/896056

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

BMC Medical Informatics and Decision Making / Nov 03, 2009

Chen, W., Smith, R., Ji, S.-Y., Ward, K. R., & Najarian, K. (2009). Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Medical Informatics and Decision Making, 9(S1). https://doi.org/10.1186/1472-6947-9-s1-s4

Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome

IEEE Journal of Biomedical and Health Informatics / Jan 01, 2019

Reamaroon, N., Sjoding, M. W., Lin, K., Iwashyna, T. J., & Najarian, K. (2019). Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome. IEEE Journal of Biomedical and Health Informatics, 23(1), 407–415. https://doi.org/10.1109/jbhi.2018.2810820

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

BMC Medical Informatics and Decision Making / Jan 14, 2009

Ji, S.-Y., Smith, R., Huynh, T., & Najarian, K. (2009). A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries. BMC Medical Informatics and Decision Making, 9(1). https://doi.org/10.1186/1472-6947-9-2

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

Scientific Reports / May 15, 2020

Bianchi, J., de Oliveira Ruellas, A. C., Gonçalves, J. R., Paniagua, B., Prieto, J. C., Styner, M., Li, T., Zhu, H., Sugai, J., Giannobile, W., Benavides, E., Soki, F., Yatabe, M., Ashman, L., Walker, D., Soroushmehr, R., Najarian, K., & Cevidanes, L. H. S. (2020). Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-64942-0

Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks

Journal of Magnetic Resonance Imaging / Feb 19, 2003

Tzacheva, A. A., Najarian, K., & Brockway, J. P. (2003). Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks. Journal of Magnetic Resonance Imaging, 17(3), 337–342. https://doi.org/10.1002/jmri.10259

An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

Computational and Mathematical Methods in Medicine / Jan 01, 2012

Belle, A., Hargraves, R. H., & Najarian, K. (2012). An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram. Computational and Mathematical Methods in Medicine, 2012, 1–12. https://doi.org/10.1155/2012/528781

Fully automated endoscopic disease activity assessment in ulcerative colitis

Gastrointestinal Endoscopy / Mar 01, 2021

Yao, H., Najarian, K., Gryak, J., Bishu, S., Rice, M. D., Waljee, A. K., Wilkins, H. J., & Stidham, R. W. (2021). Fully automated endoscopic disease activity assessment in ulcerative colitis. Gastrointestinal Endoscopy, 93(3), 728-736.e1. https://doi.org/10.1016/j.gie.2020.08.011

Private naive bayes classification of personal biomedical data: Application in cancer data analysis

Computers in Biology and Medicine / Feb 01, 2019

Wood, A., Shpilrain, V., Najarian, K., & Kahrobaei, D. (2019). Private naive bayes classification of personal biomedical data: Application in cancer data analysis. Computers in Biology and Medicine, 105, 144–150. https://doi.org/10.1016/j.compbiomed.2018.11.018

Fracture Detection in Traumatic Pelvic CT Images

International Journal of Biomedical Imaging / Jan 01, 2012

Wu, J., Davuluri, P., Ward, K. R., Cockrell, C., Hobson, R., & Najarian, K. (2012). Fracture Detection in Traumatic Pelvic CT Images. International Journal of Biomedical Imaging, 2012, 1–10. https://doi.org/10.1155/2012/327198

Detection of P, QRS, and T Components of ECG using wavelet transformation

2009 ICME International Conference on Complex Medical Engineering / Apr 01, 2009

Bsoul, A. A. R., Ji, S.-Y., Ward, K., & Najarian, K. (2009). Detection of P, QRS, and T Components of ECG using wavelet transformation. 2009 ICME International Conference on Complex Medical Engineering. https://doi.org/10.1109/iccme.2009.4906677

Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2018

Nasr-Esfahani, M., Mohrekesh, M., Akbari, M., Soroushmehr, S. M. R., Nasr-Esfahani, E., Karimi, N., Samavi, S., & Najarian, K. (2018). Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2018.8512536

Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) / Dec 01, 2010

Chen, W., Cockrell, C., Ward, K. R., & Najarian, K. (2010). Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods. 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm.2010.5706619

Fast exposure fusion using exposedness function

2017 IEEE International Conference on Image Processing (ICIP) / Sep 01, 2017

Nejati, M., Karimi, M., Soroushmehr, S. M. R., Karimi, N., Samavi, S., & Najarian, K. (2017). Fast exposure fusion using exposedness function. 2017 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2017.8296679

Symptoms of Atrial Fibrillation

Contemporary Cardiology / Jan 01, 2016

Dadkhah, S., & Sharain, K. (2016). Symptoms of Atrial Fibrillation. Short Stay Management of Atrial Fibrillation, 51–59. https://doi.org/10.1007/978-3-319-31386-3_5

Surface area-based focus criterion for multi-focus image fusion

Information Fusion / Jul 01, 2017

Nejati, M., Samavi, S., Karimi, N., Reza Soroushmehr, S. M., Shirani, S., Roosta, I., & Najarian, K. (2017). Surface area-based focus criterion for multi-focus image fusion. Information Fusion, 36, 284–295. https://doi.org/10.1016/j.inffus.2016.12.009

Automatic detection of melanoma using broad extraction of features from digital images

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Aug 01, 2016

Jafari, M. H., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Ward, K., & Najarian, K. (2016). Automatic detection of melanoma using broad extraction of features from digital images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2016.7590959

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

BMC Medical Informatics and Decision Making / Oct 15, 2012

Shandilya, S., Ward, K., Kurz, M., & Najarian, K. (2012). Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning. BMC Medical Informatics and Decision Making, 12(1). https://doi.org/10.1186/1472-6947-12-116

Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know

Anesthesiology / Oct 01, 2018

Mathis, M. R., Kheterpal, S., & Najarian, K. (2018). Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know. Anesthesiology, 129(4), 619–622. https://doi.org/10.1097/aln.0000000000002384

Low Complexity Convolutional Neural Network for Vessel Segmentation in Portable Retinal Diagnostic Devices

2018 25th IEEE International Conference on Image Processing (ICIP) / Oct 01, 2018

Hajabdollahi, M., Esfandiarpoor, R., Najarian, K., Karimi, N., Samavi, S., & Reza-Soroushmeh, S. M. (2018). Low Complexity Convolutional Neural Network for Vessel Segmentation in Portable Retinal Diagnostic Devices. 2018 25th IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2018.8451665

Efficient segmentation framework of cell images in noise environments

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Bak, E., Najarian, K., & Brockway, J. P. (n.d.). Efficient segmentation framework of cell images in noise environments. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/iembs.2004.1403538

Robust image watermarking scheme using bit-plane of hadamard coefficients

Multimedia Tools and Applications / Jan 28, 2017

Etemad, E., Samavi, S., Reza Soroushmehr, S. M., Karimi, N., Etemad, M., Shirani, S., & Najarian, K. (2017). Robust image watermarking scheme using bit-plane of hadamard coefficients. Multimedia Tools and Applications, 77(2), 2033–2055. https://doi.org/10.1007/s11042-016-4278-1

Signatures of tumor–immune interactions as biomarkers for breast cancer prognosis

Future Oncology / Jun 01, 2012

Manjili, M. H., Najarian, K., & Wang, X.-Y. (2012). Signatures of tumor–immune interactions as biomarkers for breast cancer prognosis. Future Oncology, 8(6), 703–711. https://doi.org/10.2217/fon.12.57

An image-processing enabled dental caries detection system

2009 ICME International Conference on Complex Medical Engineering / Apr 01, 2009

Olsen, G. F., Brilliant, S. S., Primeaux, D., & Najarian, K. (2009). An image-processing enabled dental caries detection system. 2009 ICME International Conference on Complex Medical Engineering. https://doi.org/10.1109/iccme.2009.4906674

Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network

2018 25th IEEE International Conference on Image Processing (ICIP) / Oct 01, 2018

Rafiei, S., Nasr-Esfahani, E., Najarian, K., Karimi, N., Samavi, S., & Soroushmehr, S. M. R. (2018). Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network. 2018 25th IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2018.8451238

Combining predictive capabilities of transcranial doppler with electrocardiogram to predict hemorrhagic shock

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society / Sep 01, 2009

Najarian, K., Hakimzadeh, R., Ward, K., Daneshvar, K., & Soo-Yeon Ji. (2009). Combining predictive capabilities of transcranial doppler with electrocardiogram to predict hemorrhagic shock. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/iembs.2009.5335394

Automated classification of Pap smear tests using neural networks

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

Zhong Li, & Najarian, K. (n.d.). Automated classification of Pap smear tests using neural networks. IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222). https://doi.org/10.1109/ijcnn.2001.938837

Boosted Dictionary Learning for Image Compression

IEEE Transactions on Image Processing / Oct 01, 2016

Nejati, M., Samavi, S., Karimi, N., Soroushmehr, S. M. R., & Najarian, K. (2016). Boosted Dictionary Learning for Image Compression. IEEE Transactions on Image Processing, 25(10), 4900–4915. https://doi.org/10.1109/tip.2016.2598483

Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2018

Akbari, M., Mohrekesh, M., Rafiei, S., Reza Soroushmehr, S. M., Karimi, N., Samavi, S., & Najarian, K. (2018). Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2018.8512226

Denoising by low-rank and sparse representations

Journal of Visual Communication and Image Representation / Apr 01, 2016

Nejati, M., Samavi, S., Derksen, H., & Najarian, K. (2016). Denoising by low-rank and sparse representations. Journal of Visual Communication and Image Representation, 36, 28–39. https://doi.org/10.1016/j.jvcir.2016.01.004

Vessel segmentation and catheter detection in X-ray angiograms using superpixels

Medical & Biological Engineering & Computing / Feb 05, 2018

Fazlali, H. R., Karimi, N., Soroushmehr, S. M. R., Shirani, S., Nallamothu, B. K., Ward, K. R., Samavi, S., & Najarian, K. (2018). Vessel segmentation and catheter detection in X-ray angiograms using superpixels. Medical & Biological Engineering & Computing, 56(9), 1515–1530. https://doi.org/10.1007/s11517-018-1793-4

An automated method for analysis of microcirculation videos for accurate assessment of tissue perfusion

BMC Medical Imaging / Dec 01, 2012

Demir, S. U., Hakimzadeh, R., Hargraves, R. H., Ward, K. R., Myer, E. V., & Najarian, K. (2012). An automated method for analysis of microcirculation videos for accurate assessment of tissue perfusion. BMC Medical Imaging, 12(1). https://doi.org/10.1186/1471-2342-12-37

Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

Biomedical Signal Processing and Control / Mar 01, 2020

Hajabdollahi, M., Esfandiarpoor, R., Sabeti, E., Karimi, N., Soroushmehr, S. M. R., & Samavi, S. (2020). Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network. Biomedical Signal Processing and Control, 57, 101792. https://doi.org/10.1016/j.bspc.2019.101792

Diabetic Wound Segmentation using Convolutional Neural Networks

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2019

Cui, C., Thurnhofer-Hemsi, K., Soroushmehr, R., Mishra, A., Gryak, J., Dominguez, E., Najarian, K., & Lopez-Rubio, E. (2019). Diabetic Wound Segmentation using Convolutional Neural Networks. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2019.8856665

An automated dental caries detection and scoring system for optical images of tooth occlusal surface

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society / Aug 01, 2014

Ghaedi, L., Gottlieb, R., Sarrett, D. C., Ismail, A., Belle, A., Najarian, K., & Hargraves, R. H. (2014). An automated dental caries detection and scoring system for optical images of tooth occlusal surface. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/embc.2014.6943988

The E-Method: a highly accurate technique for gene-expression analysis

Nature Methods / Jun 21, 2006

Tellmann, G. (2006). The E-Method: a highly accurate technique for gene-expression analysis. Nature Methods, 3(7), i–ii. https://doi.org/10.1038/nmeth894

Development and three-dimensional modelling of a biological-tissue grasper tool equipped with a tactile sensor

Canadian Journal of Electrical and Computer Engineering / Jan 01, 2005

Dargahi, J., Najarian, S., & Najarian, K. (2005). Development and three-dimensional modelling of a biological-tissue grasper tool equipped with a tactile sensor. Canadian Journal of Electrical and Computer Engineering, 30(4), 225–230. https://doi.org/10.1109/cjece.2005.1541755

Vessel region detection in coronary X-ray angiograms

2015 IEEE International Conference on Image Processing (ICIP) / Sep 01, 2015

Fazlali, H. R., Karimi, N., Soroushmehr, S. M. R., Sinha, S., Samavi, S., Nallamothu, B., & Najarian, K. (2015). Vessel region detection in coronary X-ray angiograms. 2015 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2015.7351049

Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome

BMC Medical Imaging / Oct 15, 2020

Reamaroon, N., Sjoding, M. W., Derksen, H., Sabeti, E., Gryak, J., Barbaro, R. P., Athey, B. D., & Najarian, K. (2020). Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome. BMC Medical Imaging, 20(1). https://doi.org/10.1186/s12880-020-00514-y

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Aug 01, 2016

Kadkhodaei, M., Samavi, S., Karimi, N., Mohaghegh, H., Soroushmehr, S. M. R., Ward, K., All, A., & Najarian, K. (2016). Automatic segmentation of multimodal brain tumor images based on classification of super-voxels. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2016.7592082

Transforming big data into computational models for personalized medicine and health care

Dialogues in Clinical Neuroscience / Sep 30, 2016

Reza Soroushmehr, S. M., & Najarian, K. (2016). Transforming big data into computational models for personalized medicine and health care. Dialogues in Clinical Neuroscience, 18(3), 339–343. https://doi.org/10.31887/dcns.2016.18.3/ssoroushmehr

Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach

IEEE Journal of Biomedical and Health Informatics / Mar 01, 2017

Ansari, S., Ward, K. R., & Najarian, K. (2017). Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach. IEEE Journal of Biomedical and Health Informatics, 21(2), 387–398. https://doi.org/10.1109/jbhi.2016.2524646

Predicting pelvic trauma severity using features extracted from records and X-ray and CT images

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) / Dec 01, 2010

Vasilache, S., Smith, R., Wu, J., Davuluri, P., Ward, K., Najarian, K., & Cockrell, C. (2010). Predicting pelvic trauma severity using features extracted from records and X-ray and CT images. 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). https://doi.org/10.1109/bibmw.2010.5703840

Non-invasive vascular resistance monitoring with a piezoelectric sensor and photoplethysmogram

Sensors and Actuators A: Physical / Aug 01, 2017

Wang, L., Ansari, S., Slavin, D., Ward, K., Najarian, K., & Oldham, K. R. (2017). Non-invasive vascular resistance monitoring with a piezoelectric sensor and photoplethysmogram. Sensors and Actuators A: Physical, 263, 198–208. https://doi.org/10.1016/j.sna.2017.06.007

Toward practical guideline for design of image compression algorithms for biomedical applications

Expert Systems with Applications / Sep 01, 2016

Karimi, N., Samavi, S., Soroushmehr, S. M. R., Shirani, S., & Najarian, K. (2016). Toward practical guideline for design of image compression algorithms for biomedical applications. Expert Systems with Applications, 56, 360–367. https://doi.org/10.1016/j.eswa.2016.02.047

Suppression of false arrhythmia alarms in the ICU: a machine learning approach

Physiological Measurement / Jul 25, 2016

Ansari, S., Belle, A., Ghanbari, H., Salamango, M., & Najarian, K. (2016). Suppression of false arrhythmia alarms in the ICU: a machine learning approach. Physiological Measurement, 37(8), 1186–1203. https://doi.org/10.1088/0967-3334/37/8/1186

Electrocardiogram characteristics prior to in-hospital cardiac arrest

Journal of Clinical Monitoring and Computing / Sep 19, 2014

Attin, M., Feld, G., Lemus, H., Najarian, K., Shandilya, S., Wang, L., Sabouriazad, P., & Lin, C.-D. (2014). Electrocardiogram characteristics prior to in-hospital cardiac arrest. Journal of Clinical Monitoring and Computing, 29(3), 385–392. https://doi.org/10.1007/s10877-014-9616-0

A physiological signal processing system for optimal engagement and attention detection

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) / Nov 01, 2011

Belle, A., Hobson, R., & Najarian, K. (2011). A physiological signal processing system for optimal engagement and attention detection. 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). https://doi.org/10.1109/bibmw.2011.6112429

Automated bone segmentation from Pelvic CT images

2008 IEEE International Conference on Bioinformatics and Biomeidcine Workshops / Nov 01, 2008

Vasilache, S., & Najarian, K. (2008). Automated bone segmentation from Pelvic CT images. 2008 IEEE International Conference on Bioinformatics and Biomeidcine Workshops. https://doi.org/10.1109/bibmw.2008.4686207

A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

npj Digital Medicine / May 07, 2021

Farzaneh, N., Williamson, C. A., Gryak, J., & Najarian, K. (2021). A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-021-00445-0

Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury

Artificial Intelligence in Medicine / Jul 01, 2020

Yao, H., Williamson, C., Gryak, J., & Najarian, K. (2020). Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury. Artificial Intelligence in Medicine, 107, 101910. https://doi.org/10.1016/j.artmed.2020.101910

Set of descriptors for skin cancer diagnosis using non-dermoscopic color images

2016 IEEE International Conference on Image Processing (ICIP) / Sep 01, 2016

Jafari, M. H., Samavi, S., Soroushmehr, S. M. R., Mohaghegh, H., Karimi, N., & Najarian, K. (2016). Set of descriptors for skin cancer diagnosis using non-dermoscopic color images. 2016 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2016.7532837

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Journal of Visualized Experiments / Apr 13, 2013

Chen, W., Belle, A., Cockrell, C., Ward, K. R., & Najarian, K. (2013). Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images. Journal of Visualized Experiments, 74. https://doi.org/10.3791/3871

Adaptive Specular Reflection Detection and Inpainting in Colonoscopy Video Frames

2018 25th IEEE International Conference on Image Processing (ICIP) / Oct 01, 2018

Akbari, M., Mohrekesh, M., Najariani, K., Karimi, N., Samavi, S., & Soroushmehr, S. M. R. (2018). Adaptive Specular Reflection Detection and Inpainting in Colonoscopy Video Frames. 2018 25th IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2018.8451699

Multi-modal integrated approach towards reducing false arrhythmia alarms during continuous patient monitoring: The Physionet Challenge 2015

2015 Computing in Cardiology Conference (CinC) / Sep 01, 2015

Ansari, S., Belle, A., & Najarian, K. (2015). Multi-modal integrated approach towards reducing false arrhythmia alarms during continuous patient monitoring: The Physionet Challenge 2015. 2015 Computing in Cardiology Conference (CinC). https://doi.org/10.1109/cic.2015.7411127

Coupled matrix–matrix and coupled tensor–matrix completion methods for predicting drug–target interactions

Briefings in Bioinformatics / Mar 18, 2020

Bagherian, M., Kim, R. B., Jiang, C., Sartor, M. A., Derksen, H., & Najarian, K. (2020). Coupled matrix–matrix and coupled tensor–matrix completion methods for predicting drug–target interactions. Briefings in Bioinformatics, 22(2), 2161–2171. https://doi.org/10.1093/bib/bbaa025

Automated subdural hematoma segmentation for traumatic brain injured (TBI) patients

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2017

Farzaneh, N., Soroushmehr, S. M. R., Williamson, C. A., Jiang, C., Srinivasan, A., Bapuraj, J. R., Ward, K. R., Korley, F. K., & Najarian, K. (2017). Automated subdural hematoma segmentation for traumatic brain injured (TBI) patients. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2017.8037505

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

PLOS ONE / Feb 12, 2016

Belle, A., Ansari, S., Spadafore, M., Convertino, V. A., Ward, K. R., Derksen, H., & Najarian, K. (2016). A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. PLOS ONE, 11(2), e0148544. https://doi.org/10.1371/journal.pone.0148544

Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries

Diagnostics / Sep 30, 2020

Farzaneh, N., Williamson, C. A., Jiang, C., Srinivasan, A., Bapuraj, J. R., Gryak, J., Najarian, K., & Soroushmehr, S. M. R. (2020). Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries. Diagnostics, 10(10), 773. https://doi.org/10.3390/diagnostics10100773

Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry

Informatics in Medicine Unlocked / Jan 01, 2019

Sabeti, E., Reamaroon, N., Mathis, M., Gryak, J., Sjoding, M., & Najarian, K. (2019). Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry. Informatics in Medicine Unlocked, 16, 100222. https://doi.org/10.1016/j.imu.2019.100222

Radon transform inspired method for hand gesture recognition

2016 23rd International Conference on Pattern Recognition (ICPR) / Dec 01, 2016

Khorsandi, M. A., Karimi, N., Soroushmehr, S. M. R., Hajabdollahi, M., Samavi, S., Ward, K., & Najarian, K. (2016). Radon transform inspired method for hand gesture recognition. 2016 23rd International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr.2016.7899775

Super-Resolution of 3D Magnetic Resonance Images of the Brain

Artificial Intelligence in Healthcare and Medicine / Feb 07, 2022

Domínguez, E., López-Rodríguez, D., López-Rubio, E., Maza-Quiroga, R., Molina-Cabello, M. A., & Thurnhofer-Hemsi, K. (2022). Super-Resolution of 3D Magnetic Resonance Images of the Brain. Artificial Intelligence in Healthcare and Medicine, 157–176. https://doi.org/10.1201/9781003120902-6

Hierarchical Pruning for Simplification of Convolutional Neural Networks in Diabetic Retinopathy Classification

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2019

Hajabdollahi, M., Esfandiarpoor, R., Najarian, K., Karimi, N., Samavi, S., & Reza Soroushmehr, S. M. (2019). Hierarchical Pruning for Simplification of Convolutional Neural Networks in Diabetic Retinopathy Classification. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2019.8857769

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Computational and Mathematical Methods in Medicine / Jan 01, 2012

Davuluri, P., Wu, J., Tang, Y., Cockrell, C. H., Ward, K. R., Najarian, K., & Hargraves, R. H. (2012). Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries. Computational and Mathematical Methods in Medicine, 2012, 1–12. https://doi.org/10.1155/2012/898430

Segmentation of ventricles in brain CT images using Gaussian Mixture Model method

2009 ICME International Conference on Complex Medical Engineering / Apr 01, 2009

Chen, W., & Najarian, K. (2009). Segmentation of ventricles in brain CT images using Gaussian Mixture Model method. 2009 ICME International Conference on Complex Medical Engineering. https://doi.org/10.1109/iccme.2009.4906676

Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach

Anesthesia & Analgesia / May 01, 2020

Mathis, M. R., Engoren, M. C., Joo, H., Maile, M. D., Aaronson, K. D., Burns, M. L., Sjoding, M. W., Douville, N. J., Janda, A. M., Hu, Y., Najarian, K., & Kheterpal, S. (2020). Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesthesia & Analgesia, 130(5), 1188–1200. https://doi.org/10.1213/ane.0000000000004630

Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images

IEEE Transactions on Multimedia / Nov 01, 2017

Karimi, M., Nejati, M., Soroushmehr, S. M. R., Samavi, S., Karimi, N., & Najarian, K. (2017). Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images. IEEE Transactions on Multimedia, 19(11), 2475–2489. https://doi.org/10.1109/tmm.2017.2699082

Image processing and machine learning for diagnostic analysis of microcirculation

2009 ICME International Conference on Complex Medical Engineering / Apr 01, 2009

Demir, S., Mirshahi, N., Tiba, M. H., Draucker, G., Ward, K., Hobson, R., & Najarian, K. (2009). Image processing and machine learning for diagnostic analysis of microcirculation. 2009 ICME International Conference on Complex Medical Engineering. https://doi.org/10.1109/iccme.2009.4906669

Segmentation of bleeding regions in wireless capsule endoscopy for detection of informative frames

Biomedical Signal Processing and Control / Aug 01, 2019

Hajabdollahi, M., Esfandiarpoor, R., Khadivi, P., Soroushmehr, S. M. R., Karimi, N., Najarian, K., & Samavi, S. (2019). Segmentation of bleeding regions in wireless capsule endoscopy for detection of informative frames. Biomedical Signal Processing and Control, 53, 101565. https://doi.org/10.1016/j.bspc.2019.101565

Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation

IEEE Transactions on Circuits and Systems for Video Technology / Mar 01, 2019

Mohaghegh, H., Karimi, N., Soroushmehr, S. M. R., Samavi, S., & Najarian, K. (2019). Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 29(3), 683–697. https://doi.org/10.1109/tcsvt.2018.2808682

Classifying osteosarcoma patients using machine learning approaches

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2017

Li, Z., Soroushmehr, S. M. R., Hua, Y., Mao, M., Qiu, Y., & Najarian, K. (2017). Classifying osteosarcoma patients using machine learning approaches. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2017.8036768

Quality assessment of retargeted images by salient region deformity analysis

Journal of Visual Communication and Image Representation / Feb 01, 2017

Karimi, M., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Lin, W., & Najarian, K. (2017). Quality assessment of retargeted images by salient region deformity analysis. Journal of Visual Communication and Image Representation, 43, 108–118. https://doi.org/10.1016/j.jvcir.2016.12.011

Heart rate variability analysis during central hypovolemia using wavelet transformation

Journal of Clinical Monitoring and Computing / Feb 01, 2013

Ji, S.-Y., Belle, A., Ward, K. R., Ryan, K. L., Rickards, C. A., Convertino, V. A., & Najarian, K. (2013). Heart rate variability analysis during central hypovolemia using wavelet transformation. Journal of Clinical Monitoring and Computing, 27(3), 289–302. https://doi.org/10.1007/s10877-013-9434-9

An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process

Computational and Mathematical Methods in Medicine / Jan 01, 2013

Oswal, V., Belle, A., Diegelmann, R., & Najarian, K. (2013). An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process. Computational and Mathematical Methods in Medicine, 2013, 1–10. https://doi.org/10.1155/2013/592790

Interactive visual analysis of time-series microarray data

The Visual Computer / Jan 08, 2008

Jeong, D. H., Darvish, A., Najarian, K., Yang, J., & Ribarsky, W. (2008). Interactive visual analysis of time-series microarray data. The Visual Computer, 24(12), 1053–1066. https://doi.org/10.1007/s00371-007-0205-9

Biomedical Image Segmentation Based on Shape Stability

2007 IEEE International Conference on Image Processing / Jan 01, 2007

Li, Z., & Najarian, K. (2007). Biomedical Image Segmentation Based on Shape Stability. 2007 IEEE International Conference on Image Processing. https://doi.org/10.1109/icip.2007.4379576

Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Jul 01, 2019

Bagheri, M., Mohrekesh, M., Tehrani, M., Najarian, K., Karimi, N., Samavi, S., & Reza Soroushmehr, S. M. (2019). Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2019.8856793

Adaptive Real-Time Removal of Impulse Noise in Medical Images

Journal of Medical Systems / Oct 02, 2018

HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, R., Shirani, S., Najarian, K., & Samavi, S. (2018). Adaptive Real-Time Removal of Impulse Noise in Medical Images. Journal of Medical Systems, 42(11). https://doi.org/10.1007/s10916-018-1074-7

Vessel segmentation in low contrast X-ray angiogram images

2016 IEEE International Conference on Image Processing (ICIP) / Sep 01, 2016

Felfelian, B., Fazlali, H. R., Karimi, N., Soroushmehr, S. M. R., Samavi, S., Nallamothu, B., & Najarian, K. (2016). Vessel segmentation in low contrast X-ray angiogram images. 2016 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2016.7532382

Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation

International Journal of Data Mining and Bioinformatics / Jan 01, 2013

Chen, W., Cockrell, C. H., Ward, K., & Najarian, K. (2013). Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation. International Journal of Data Mining and Bioinformatics, 8(4), 480. https://doi.org/10.1504/ijdmb.2013.056617

PAC learning in non-linear FIR models

International Journal of Adaptive Control and Signal Processing / Jan 01, 2001

Najarian, K., Dumont, G. A., Davies, M. S., & Heckman, N. E. (2001). PAC learning in non-linear FIR models. International Journal of Adaptive Control and Signal Processing, 15(1), 37–52. https://doi.org/10.1002/1099-1115(200102)15:1<37::aid-acs626>3.0.co;2-7

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications

Seminars in Orthodontics / Jun 01, 2021

Bianchi, J., Ruellas, A., Prieto, J. C., Li, T., Soroushmehr, R., Najarian, K., Gryak, J., Deleat-Besson, R., Le, C., Yatabe, M., Gurgel, M., Turkestani, N. A., Paniagua, B., & Cevidanes, L. (2021). Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications. Seminars in Orthodontics, 27(2), 78–86. https://doi.org/10.1053/j.sodo.2021.05.004

Increasing efficiency of SVMp+ for handling missing values in healthcare prediction

PLOS Digital Health / Jun 29, 2023

Zhang, Y., Gao, Z., Wittrup, E., Gryak, J., & Najarian, K. (2023). Increasing efficiency of SVMp+ for handling missing values in healthcare prediction. PLOS Digital Health, 2(6), e0000281. https://doi.org/10.1371/journal.pdig.0000281

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

IEEE Journal of Biomedical and Health Informatics / Jan 01, 2023

Yao, H., Derksen, H., Golbus, J. R., Zhang, J., Aaronson, K. D., Gryak, J., & Najarian, K. (2023). A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies. IEEE Journal of Biomedical and Health Informatics, 27(1), 239–250. https://doi.org/10.1109/jbhi.2022.3211765

Evaluation of Capacitive ECG for Unobtrusive Atrial Fibrillation Monitoring

IEEE Sensors Letters / Jan 01, 2023

Zhang, W., Li, Z., Gryak, J., Gunaratne, P., Wittrup, E., & Najarian, K. (2023). Evaluation of Capacitive ECG for Unobtrusive Atrial Fibrillation Monitoring. IEEE Sensors Letters, 1–4. https://doi.org/10.1109/lsens.2023.3315223

Detection of Low Cardiac Index Using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks

IEEE Sensors Journal / Jul 01, 2021

Ansari, S., Golbus, J. R., Tiba, M. H., Mccracken, B., Wang, L., Aaronson, K. D., Ward, K. R., Najarian, K., & Oldham, K. R. (2021). Detection of Low Cardiac Index Using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks. IEEE Sensors Journal, 21(13), 14281–14289. https://doi.org/10.1109/jsen.2020.3022273

Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome

IEEE Journal of Biomedical and Health Informatics / Mar 01, 2021

Sabeti, E., Drews, J., Reamaroon, N., Warner, E., Sjoding, M. W., Gryak, J., & Najarian, K. (2021). Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome. IEEE Journal of Biomedical and Health Informatics, 25(3), 784–796. https://doi.org/10.1109/jbhi.2020.3008601

Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations

IEEE/ACM Transactions on Computational Biology and Bioinformatics / May 01, 2020

Zare, F., Ansari, S., Najarian, K., & Nabavi, S. (2020). Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(3), 868–876. https://doi.org/10.1109/tcbb.2018.2869738

Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units

Entropy / Mar 12, 2018

Afghah, F., Razi, A., Soroushmehr, R., Ghanbari, H., & Najarian, K. (2018). Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units. Entropy, 20(3), 190. https://doi.org/10.3390/e20030190

Education

University of British Columbia

Ph.D., Electrical and Computer Engineering / August, 2000

Vancouver, British Columbia, Canada

Experience

University of Michigan, Ann Arbor

Professor

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