S
Saeed Ashrafinia
Researcher at Johns Hopkins University
Publications - 44
Citations - 2193
Saeed Ashrafinia is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Evolutionary algorithm & Computational complexity theory. The author has an hindex of 13, co-authored 41 publications receiving 1058 citations. Previous affiliations of Saeed Ashrafinia include Johns Hopkins University School of Medicine & University of Texas Southwestern Medical Center.
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Journal ArticleDOI
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg,Alex Zwanenburg,Martin Vallières,Mahmoud A. Abdalah,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Vincent Andrearczyk,Aditya Apte,Saeed Ashrafinia,Spyridon Bakas,Roelof J. Beukinga,Ronald Boellaard,Marta Bogowicz,Luca Boldrini,Irène Buvat,Gary Cook,Christos Davatzikos,Adrien Depeursinge,Marie-Charlotte Desseroit,Nicola Dinapoli,Cuong V. Dinh,Sebastian Echegaray,Issam El Naqa,Issam El Naqa,Andriy Fedorov,Roberto Gatta,Robert J. Gillies,Vicky Goh,Michael Götz,Matthias Guckenberger,Sung Min Ha,Mathieu Hatt,Fabian Isensee,Philippe Lambin,Stefan Leger,Stefan Leger,Ralph T.H. Leijenaar,Jacopo Lenkowicz,Fiona Lippert,Are Losnegård,Klaus H. Maier-Hein,Olivier Morin,Henning Müller,Sandy Napel,Christophe Nioche,Fanny Orlhac,Sarthak Pati,Elisabeth Pfaehler,Arman Rahmim,Arman Rahmim,Arvind Rao,Jonas Scherer,Muhammad Siddique,Nanna M. Sijtsema,Jairo Socarras Fernandez,Emiliano Spezi,Roel J H M Steenbakkers,Stephanie Tanadini-Lang,Daniela Thorwarth,Esther G.C. Troost,Esther G.C. Troost,Taman Upadhaya,Vincenzo Valentini,Lisanne V. van Dijk,Joost J. M. van Griethuysen,Floris H. P. van Velden,Philip Whybra,Christian Richter,Christian Richter,Steffen Löck,Steffen Löck +70 more
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
Journal ArticleDOI
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Isaac Shiri,Hassan Maleki,Ghasem Hajianfar,Hamid Abdollahi,Saeed Ashrafinia,Mathieu Hatt,Mehrdad Oveisi,Arman Rahmim +7 more
TL;DR: It is demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients and showed improved predictive power relative to conventional image-derived metrics.
Journal ArticleDOI
Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer
TL;DR: Fusion radiomics modeling showed varying improvements compared to single modality models for different outcome predictions in different partitions, highlighting the importance of generalizing radiomics models.
Journal ArticleDOI
Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images
Dongyang Du,Hui Feng,Wenbing Lv,Saeed Ashrafinia,Qingyu Yuan,Quanshi Wang,Wei Yang,Qianjin Feng,Wufan Chen,Arman Rahmim,Arman Rahmim,Lijun Lu +11 more
TL;DR: This study identified the most accurate and reliable machine learning methods, which could enhance the application of radiomics methods in the precision of diagnosis of NPC.
Journal ArticleDOI
A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.
Kevin H. Leung,Wael Marashdeh,Rick Wray,Saeed Ashrafinia,Martin G. Pomper,Arman Rahmim,Arman Rahmim,Abhinav K. Jha +7 more
TL;DR: An automated physics-guided deep-learning-based PET-segmentation framework to segment PET images on a per-slice basis and yielded reliable performance in delineating tumors in FDG-PET images of patients with lung cancer.