Postdoctoral Scholar
Atlas is a Postdoctoral Scholar in the lab, focusing on the applications of deep learning and advanced MRI techniques in Diffuse Midline Glioma, a rare pediatric tumor. She completed her M.D. degree from Mashhad University of Medical Sciences (MUMS) in Iran and previously worked on cancer radiomics and radiogenomics combined with machine learning. In her free time, she enjoys going on a run or hike with friends, dancing, and playing the piano
Publications
Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.
AJNR. American journal of neuroradiology
Artificial Intelligence Applications in Musculoskeletal Imaging.
Current reviews in musculoskeletal medicine
Applications of Artificial Intelligence and Advanced Imaging in Pediatric Diffuse Midline Glioma.
Neuro-oncology
Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification.
Journal of imaging informatics in medicine
Medical Image Segmentation Review: The Success of U-Net.
IEEE transactions on pattern analysis and machine intelligence
The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.
Annals of nuclear medicine
Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.
Medical physics
Machine learning based readmission and mortality prediction in heart failure patients.
Scientific reports
Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.
La Radiologia medica
High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.
Scientific reports
Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.
Journal of digital imaging
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.
Computers in biology and medicine