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Alireza Mehrtash
Researcher at Brigham and Women's Hospital
Publications - 40
Citations - 2270
Alireza Mehrtash is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Segmentation & Deep learning. The author has an hindex of 16, co-authored 39 publications receiving 1298 citations. Previous affiliations of Alireza Mehrtash include Harvard University & University of British Columbia.
Papers
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Journal ArticleDOI
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Wenya Linda Bi,Ahmed Hosny,Matthew B. Schabath,Maryellen L. Giger,Nicolai Juul Birkbak,Nicolai Juul Birkbak,Alireza Mehrtash,Alireza Mehrtash,Tavis Allison,Tavis Allison,Omar Arnaout,Christopher Abbosh,Christopher Abbosh,Ian F. Dunn,Raymond H. Mak,Rulla M. Tamimi,Clare M. Tempany,Charles Swanton,Charles Swanton,Udo Hoffmann,Lawrence H. Schwartz,Lawrence H. Schwartz,Robert J. Gillies,Raymond Y. Huang,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +25 more
TL;DR: The authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types to illustrate how common clinical problems are being addressed.
Book ChapterDOI
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Mohsen Ghafoorian,Mohsen Ghafoorian,Alireza Mehrtash,Alireza Mehrtash,Tina Kapur,Nico Karssemeijer,Elena Marchiori,Mehran Pesteie,Charles R.G. Guttmann,Frank-Erik de Leeuw,Clare M. Tempany,Bram van Ginneken,Andriy Fedorov,Purang Abolmaesumi,Bram Platel,William M. Wells +15 more
TL;DR: In this paper, a CNN was trained on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain, and compared the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.
Journal ArticleDOI
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf,Adrià Casamitjana,D. Louis Collins,Mahsa Dadar,Achilleas Georgiou,Mohsen Ghafoorian,Dakai Jin,April Khademi,Jesse Knight,Hongwei Li,Xavier Lladó,J. Matthijs Biesbroek,Miguel Luna,Qaiser Mahmood,Richard McKinley,Alireza Mehrtash,Sebastien Ourselin,Bo-yong Park,Hyunjin Park,Sang-Hyun Park,Simon Pezold,Elodie Puybareau,Jeroen de Bresser,Leticia Rittner,Carole H. Sudre,Sergi Valverde,Verónica Vilaplana,Roland Wiest,Yongchao Xu,Ziyue Xu,Guodong Zeng,Jianguo Zhang,Guoyan Zheng,Rutger Heinen,Christopher Chen,Wiesje M. van der Flier,Frederik Barkhof,Max A. Viergever,Geert Jan Biessels,Simon Andermatt,Mariana P. Bento,Matt Berseth,Mikhail Belyaev,M. Jorge Cardoso +43 more
TL;DR: There is a cluster of four methods that rank significantly better than the other methods, with one clear winner, and the inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners.
Journal ArticleDOI
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf,J. Matthijs Biesbroek,Jeroen de Bresser,Rutger Heinen,Simon Andermatt,Mariana P. Bento,Matt Berseth,Mikhail Belyaev,M. Jorge Cardoso,Adrià Casamitjana,D. Louis Collins,Mahsa Dadar,Achilleas Georgiou,Mohsen Ghafoorian,Dakai Jin,April Khademi,Jesse Knight,Hongwei Li,Xavier Lladó,Miguel Luna,Qaiser Mahmood,Richard McKinley,Alireza Mehrtash,Sebastien Ourselin,Bo-yong Park,Hyunjin Park,Sang-Hyun Park,Simon Pezold,Elodie Puybareau,Leticia Rittner,Carole H. Sudre,Sergi Valverde,Verónica Vilaplana,Roland Wiest,Yongchao Xu,Ziyue Xu,Guodong Zeng,Jianguo Zhang,Guoyan Zheng,Christopher Chen,Wiesje M. van der Flier,Frederik Barkhof,Max A. Viergever,Geert Jan Biessels +43 more
TL;DR: The WMH segmentation challenge as discussed by the authors was the first attempt to evaluate the performance of automatic segmentation of cerebral white matter hyperintensities (WMH) of presumed vascular origin.
Journal ArticleDOI
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
TL;DR: In this article, the authors compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of fully convolutional neural networks (FCNs) for medical image segmentation.