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Elham Saeedzadeh

Showing results (1-10 of 7) with videos related to

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Computers in Biology and Medicine|March 11, 2025
Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo studyMahsa Noorvand, Farshid Babapour Mofrad, Elham Saeedzadeh
Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)|April 24, 2026
Statistical shape modeling as a practical tool for dosimetry: quantifying uncertainty in internal dose assessmentMahsa Noorvand, Farshid Babapour Mofrad, Elham Saeedzadeh
Journal of Cancer Research and Therapeutics|July 5, 2025
Deep learning-based organ-at-risk segmentation, registration and dosimetry on cone beam computed tomography images in radiation therapy: A comprehensive reviewEzatsadat Fakhar, Azam Janat Esfahani, Elham Saeedzadeh, et al.
Scientific Reports|October 3, 2025
Photothermal therapy and radiotherapy of Bi<sub>2</sub>S<sub>3</sub>-GNRs hybrid nanoparticles in treatment of breast cancerFaranak Saghatchi, Farshid Babapour Mofrad, Hossein Danafar, et al.
Zeitschrift Fur Medizinische Physik|March 9, 2020
A new formulation of polymer gel dosimeter with reduced toxicity: Dosimetric characteristics and radiological propertiesAbdulrahman Rashidi, Seyed Mohammad Mahdi Abtahi, Elham Saeedzadeh, et al.
Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine|May 29, 2026
Clinically reliable and stable automated segmentation of DLBCL lesions on PET/CT using self-configuring nnU-Net for robust TMTV quantificationSajad Keshavarz, Elham Saeedzadeh, Hossein Arabi, et al.
Cancer Treatment and Research Communications|February 20, 2026
From pixels to prognosis: A QUADAS-2-Guided systematic review and meta-analysis of deep learning segmentation for DLBCL in PET and PET/CTSajad Keshavarz, Elham Saeedzadeh, Hossein Arabi, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Computers in Biology and Medicine|March 11, 2025
Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo studyMahsa Noorvand, Farshid Babapour Mofrad, Elham Saeedzadeh
Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)|April 24, 2026
Statistical shape modeling as a practical tool for dosimetry: quantifying uncertainty in internal dose assessmentMahsa Noorvand, Farshid Babapour Mofrad, Elham Saeedzadeh
Journal of Cancer Research and Therapeutics|July 5, 2025
Deep learning-based organ-at-risk segmentation, registration and dosimetry on cone beam computed tomography images in radiation therapy: A comprehensive reviewEzatsadat Fakhar, Azam Janat Esfahani, Elham Saeedzadeh, et al.
Scientific Reports|October 3, 2025
Photothermal therapy and radiotherapy of Bi<sub>2</sub>S<sub>3</sub>-GNRs hybrid nanoparticles in treatment of breast cancerFaranak Saghatchi, Farshid Babapour Mofrad, Hossein Danafar, et al.
Zeitschrift Fur Medizinische Physik|March 9, 2020
A new formulation of polymer gel dosimeter with reduced toxicity: Dosimetric characteristics and radiological propertiesAbdulrahman Rashidi, Seyed Mohammad Mahdi Abtahi, Elham Saeedzadeh, et al.
Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine|May 29, 2026
Clinically reliable and stable automated segmentation of DLBCL lesions on PET/CT using self-configuring nnU-Net for robust TMTV quantificationSajad Keshavarz, Elham Saeedzadeh, Hossein Arabi, et al.
Cancer Treatment and Research Communications|February 20, 2026
From pixels to prognosis: A QUADAS-2-Guided systematic review and meta-analysis of deep learning segmentation for DLBCL in PET and PET/CTSajad Keshavarz, Elham Saeedzadeh, Hossein Arabi, et al.
Pageof 1