Gökhan Ertaş

Title:

Associate Prof. Dr.

Department:

Biomedical Engineering Department

Position:

Department Head

Email:

gokhan.ertas@yeditepe.edu.tr

Telephone:

+90 216 578 0000-1497

Time:

Full Time

Akademik

Resume

Research Interests

Biomedical Instrumentation
Medical Imaging and Image Computing
Deep Learning and Decision Making in Clinical Medicine

Education

BS: Erciyes Univ. 1999
MS: Boğaziçi Univ. 2001
PhD. Boğaziçi Univ. 2007

Courses

Fall
BME302 Biomedical Electronics II
BME441 Microprocessors and Microcontrollers in BME
BME400 Summer Practice
BME492 Engineering Project

BME517 Rehabilitation Engineering
BME518 Medical Informatics

Spring
BME302 Biomedical Electronics II
BME314 Biomedical Instrumentation
BME324 Biomedical Sensors and Transducers
BME441 Microprocessors and Microcontrollers in BME
BME492 Engineering Project

Biography

Gokhan is currently an Associate Professor, the chair of the Biomedical Engineering Department, the deputy chair of the Non-Interventional Clinical Research Ethics Board, and the board member of the Ethics Committee for Clinical Research at Yeditepe University.

Gokhan received his PhD and MSc degrees in Biomedical Engineering both from the Institute of Biomedical Engineering, Bogazici University, Turkey, in 2007 (as top-ranked), and BS degree in Electronics Engineering from Erciyes University, Turkey in 1999.

At Yeditepe University, Gokhan focuses his attention on developing smart home healthcare devices, assistive mobility devices and wearable devices for physiological activity monitoring and recognition. He also leads the efforts in developing deep learning models for accurate diagnosis of prostate cancer from MR images. Gokhan instructs many courses such as “Medical Informatics”, “Rehabilitation Engineering”, “Biomedical Instrumentation” and “Medical Imaging” and works on improving biomedical engineering education in Turkey.

Gokhan has been the researcher, and consultant for many European Union or TUBITAK granted research projects. He has been in collaboration with colleagues from Marmara University, Istanbul University, Acibadem University and Koç University and published many conference and journal papers.

Gokhan previously worked as a researcher at The Institute of Cancer Research UK (as top-ranked) and The Royal Marsden Hospital in between 2007-2010. He managed research in developing computational methods to comparing breast MR images, joint with University of Oxford, University of Manchester and University College London.

During his graduate study at Bogazici University, Gokhan was the researcher in projects with a goal of developing novel image computing algorithms to diagnosing breast cancer using X-ray imaging and MR Imaging. In 2006, he developed “DynaMammoAnalyst”, an interactive dynamic analysis and decision support software for contrast-enhanced breast MR imaging.

Publications

Selected Papers

B.O. Demirci, O. Bugdayci, G. Ertas, D.E.T. Sanlı, H. Kaya, E. Arıbal (2023). Linear Regression Modeling Based Scoring System to Reduce Benign Breast Biopsies Using Multi-parametric US with Color Doppler and SWE. Acad Radiol. 2023 Feb 16:S1076-6332(23)00039-9. doi: 10.1016/j.acra.2023.01.024..
S. Azamat, Ş. Karaman, I.F. Azamat, G. Ertas, C.B. Kulle, M. Keskin, R. Sakin, B. Bakır, E.N Oral, M.G. Kartal (2022). Complete Response Evaluation of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy using Textural Features Obtained from T2 Weighted Imaging and ADC Maps. Current Medical Imaging, doi: 10.2174/ 1573405618666220303111026.
G. Ertas (2021). Attenuation Model Free Classification of Diffusion MR Signals of the Breast Tissue using Long Short-Term Memory Networks. Balkan Journal of Electrical and Computer Engineering, 9(3), 278-283
A. Onay, G. Ertas, M. Vural, E. Colak, T. Esen, B. Bakir (2020). The role of T2-weighted images in assessing the grade of extraprostatic extension of the prostate carcinoma. Abdominal Radiology, 45:3293-3300.
G. Ertas (2020). Development of a Radial Basis Function Neural Network Model to Predict High b-value Diffusion MR Signals of the Prostate. International Journal of Intelligent Systems and Applications in Engineering, 8(2), 45-51.
G. Ertas (2019). Estimating The Distributed Diffusion Coefficient of Breast Tissue in Diffusion-Weighted Imaging using Multilayer Perceptrons. Soft Computing, 23:7821-7830.
G. Ertas (2019). Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue Using Particle Swarm Optimization. An International Journal of Optimization and Control: Theories Applications, 9(2):105-112.
G. Ertas (2018). Detection of High GS Risk Group Prostate Tumors by Diffusion Tensor Imaging and Logistic Regression Modelling. Magnetic Resonance Imaging, 50:125-133.
G. Ertas (2018). Neural Network Based Classification of Melanocytic Lesions in Dermoscopy: Role of Input Vector Encoding. Electrica, 18(2):242-248.
G. Ertas, C. Onaygil, O. Bugdayci, E. Aribal (2018). Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings. European Journal of Breast Health, 14:85-92.
A. Onay, G. Ertas, M. Vural, O. Acar, Y. Salicam, B. Coskun, S. Akpek (2017). Evaluation of Peripheral Zone Prostate Cancer Aggressiveness using the Ratio of Diffusion Tensor Imaging Measures. Contrast Media Molecular Imaging. Contrast Media & Molecular Imaging: 5678350.
G. Ertas, S. Doran and M.O. Leach (2017). A Computerized Volumetric Segmentation Method Applicable to Multi-Centre MRI Data to Support Computer-Aided Breast Tissue Analysis, Density Assessment and Lesion Localization. Medical & Biological Engineering & Computing, 55(1):57-68.
G. Ertas, C. Onaygil, Y. Akin, H. Kaya and E. Aribal (2016). Quantitative Differentiation of Breast Lesions at 3T Diffusion-Weighted Imaging (DWI) using The Ratio of Distributed Diffusion Coefficient (DDC). Journal of Magnetic Resonance Imaging, 44(6):1633-1641.
S. Bozkurt and G. Ertas (2015). Comparison of Left and Right Fingertip PPG Signals Using Signal Power Estimates and Poincare Indexes. American Journal of Biomedical and Life Sciences, 3:6-10.
M. Vural, G. Ertas, A. Onay, O. Acar, T. Esen, Y. Saglican, H.P. Zengingonul and S. Akpek, (2014). Conspicuity of Peripheral Zone Prostate Cancer on Computed Diffusion-Weighted Imaging: Comparison of cDWI1500, cDWI2000, and cDWI3000. Biomed Res Int: 768291.
M.A. Schmidt, M. Borri, E. Scurr, G. Ertas, G. Payne, E. O’Flynn, N. deSouza and M. O. Leach (2013). Breast dynamic contrast-enhanced examinations with fat suppression: Are contrast-agent uptake curves affected by magnetic field inhomogeneity?. European Radiology, 23:1537–1545.
G. Ertas, S. Doran and M.O. Leach (2011). Computerized Detection of Breast Lesions in Multi-Centre and Multi-Instrument DCE-MR Data using 3D Principal Component Maps and Template Matching. Physics in Medicine and Biology, 56:7795–7811.
G. Ertas, H.O. Gulcur and M. Tunaci (2008). An Interactive Dynamic Analysis and Decision Support Software for MR Mammography. Computerized Medical Imaging and Graphics, 32(4):284-293.
G. Ertas, H.O. Gulcur and M. Tunaci (2008). Normalized Maximum Intensity Time Ratio Maps and Morphological Descriptors for Assessment of Malignancy in MR Mammography. Medical Physics, 35(5):1-8.
G. Ertas, H.O. Gulcur, M. Tunaci, O. Osman and O.N. Ucan (2008). A Preliminary Study on Computerized Lesion Localization in MR Mammography using 3D nMITR Maps, Multilayer Cellular Neural Networks and Fuzzy c-Partitioning. Medical Physics, 35(1):195-205.
G. Ertas, H.O. Gulcur, O. Osman, O.N. Ucan, M. Tunaci and M. Dursun (2008). Breast MR Segmentation and Lesion Detection with Cellular Neural Networks and 3D Template Matching. Computers in Medicine and Biology, 38(1):116-126.
G. Ertas, H.O. Gulcur and M. Tunaci (2007). Improved Lesion Detection in MR Mammography: Three-Dimensional Segmentation, Moving Voxel Sampling, and Normalized Maximum Intensity–Time Ratio Entropy", Academic Radiology, 14(2):151-161.

Search form

Gökhan Ertaş

Akademik