Kayvan Najarian
Professor of Comp Med and Bioinf, Emergency Med, and Electrical and Comp Engineering
Research Interests
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
Experience
University of Michigan, Ann Arbor
Professor
Links & Social Media
Join Kayvan on NotedSource!
Join Now
At NotedSource, we believe that professors, post-docs, scientists and other researchers have deep, untapped knowledge and expertise that can be leveraged to drive innovation within companies. NotedSource is committed to bridging the gap between academia and industry by providing a platform for collaboration with industry and networking with other researchers.
For industry, NotedSource identifies the right academic experts in 24 hours to help organizations build and grow. With a platform of thousands of knowledgeable PhDs, scientists, and industry experts, NotedSource makes connecting and collaborating easy.
For academic researchers such as professors, post-docs, and Ph.D.s, NotedSource provides tools to discover and connect to your colleagues with messaging and news feeds, in addition to the opportunity to be paid for your collaboration with vetted partners.
Expert Institutions
Proudly trusted by
