Mr. Mahmoud Marhamati ,PhD candidate in Tehran University of Medical Science, Iran
š Education :
M. Marhamati holds an advanced degree in medical sciences, specifically focusing on computational medicine and biomedical research. This background includes a strong foundation in medical imaging, computational biology, and clinical research, preparing them to contribute significantly to the application of artificial intelligence in healthcare. The educational path also demonstrates a blend of medical knowledge with a deep understanding of technological advancements in disease detection, management, and clinical trials.
š¢Ā Experience:
Conclusion:
Mr. Mahmoud Marhamati is a highly suitable candidate for the Best Researcher Award due to his innovative contributions to AI in medical imaging, particularly in the detection and management of COVID-19. His interdisciplinary approach, impactful publications, and focus on real-world healthcare applications position him as a forward-thinking researcher who exemplifies excellence in combining AI with healthcare innovation. To further strengthen his candidacy, expanding into other AI applications beyond COVID-19 and seeking leadership opportunities would broaden his impact
Publications :
- Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
- Authors: M. Momeny, A.A. Neshat, M.A. Hussain, S. Kia, M. Marhamati, et al.
- Journal: Computers in Biology and Medicine
- Year: 2021
- Citations: 67
- Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images
- Authors: A. Akbarimajd, N. Hoertel, M.A. Hussain, A.A. Neshat, M. Marhamati, et al.
- Journal: Journal of Computational Science
- Year: 2022
- Citations: 36
- Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy
- Authors: M. Momeny, A.A. Neshat, A. Gholizadeh, A. Jafarnezhad, E. Rahmanzadeh, et al.
- Journal: Computers in Biology and Medicine
- Year: 2022
- Citations: 32
- Retracted: Internet of things in the management of chronic diseases during the COVIDā19 pandemic: A systematic review
- Authors: A. Shamsabadi, Z. Pashaei, A. Karimi, P. Mirzapour, K. Qaderi, M. Marhamati, et al.
- Journal: Health Science Reports
- Year: 2022
- Citations: 27
- LAIU-Net: a learning-to-augment incorporated robust U-Net for depressed humansā tongue segmentation
- Authors: M. Marhamati, A.A.L. Zadeh, M.M. Fard, M.A. Hussain, K. Jafarnezhad, et al.
- Journal: Displays
- Year: 2023
- Citations: 18
- Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT
- Authors: M.A. Hussain, Z. Mirikharaji, M. Momeny, M. Marhamati, A.A. Neshat, R. Garbi, et al.
- Journal: Computerized Medical Imaging and Graphics
- Year: 2022
- Citations: 11
- Comparing Serum Levels of Vitamin D and Zinc in Novel CoronavirusāInfected Patients and Healthy Individuals in Northeastern Iran, 2020
- Authors: S.J. Hosseini, B. Moradi, M. Marhamati, A.A. Firouzian, E. Ildarabadi, A. Abedi, et al.
- Journal: Infectious Diseases in Clinical Practice
- Year: 2021
- Citations: 6
- Comparing the effects of pulsatile and continuous flushing on time and type of peripheral intravenous catheters patency: a randomized clinical trial
- Authors: S.J. Hosseini, F. Eidy, M. Kianmehr, A.A. Firouzian, F. Hajiabadi, M. Marhamati, et al.
- Journal: Journal of Caring Sciences
- Year: 2021
- Citations: 4
- Emergency Medical Service Personnel Satisfaction Regarding Ambulance Service Facilities and Welfare
- Authors: A. Jesmi, H.M. Ziyarat, M. Marhamati, T. Mollaei, H. Chenari
- Journal: Iranian Journal of Emergency Medicine
- Year: 2015
- Citations: 2
- Patient’s airway monitoring during cardiopulmonary resuscitation using deep networks
- Authors: M. Marhamati, B. Dorry, S. Imannezhad, M.A. Hussain, A.A. Neshat, et al.
- Journal: Medical Engineering & Physics
- Year: 2024
- Citations: 1
- Comparison of using cold versus regular temperature tube on successful nasogastric intubation for patients in toxicology emergency department: a randomized clinical trial
- Authors: S.R. Mazlom, A.A. Firouzian, H.M. Norozi, A.G. Toussi, M. Marhamati
- Journal: Journal of Caring Sciences
- Year: 2020
- Citations: 1
- Comparison of using cooled and regular-temperature nasogastric tubes on the success of nasogastric intubation
- Authors: S. Mazlom, M. Marhamati, H. Norozi, A. Ghasemi Toosi
- Journal: Evidence Based Care
- Year: 2015
- Citations: 1