Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study-Reference-Cited by-同舟云学术

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Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

Published:2019-02-28 Issue:1 Volume:19 Page:
ISSN:1471-2342
Container-title:BMC Medical Imaging
language:en
Short-container-title:BMC Med Imaging

Author:

Viswanath Satish E.^ORCID,Chirra Prathyush V.,Yim Michael C.,Rofsky Neil M.,Purysko Andrei S.,Rosen Mark A.,Bloch B Nicolas,Madabhushi Anant

Funder

National Institutes of Health

National Center for Research Resources

DOD/CDMRP

National Institute of Diabetes and Digestive and Kidney Diseases

National Institute of Biomedical Imaging and Bioengineering

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging

Link

http://link.springer.com/content/pdf/10.1186/s12880-019-0308-6.pdf

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3. Juntu J, Sijbers J, De Backer S, Rajan J, Van Dyck D. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging. 2010; 31(3):680–9.

4. Wei L, Yang Y, Nishikawa RM, Jiang Y. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging. 2005; 24(3):371–80.

5. Herlidou-Meme S, Constans JM, Carsin B, Olivie D, Eliat PA, Nadal-Desbarats L, Gondry C, Le Rumeur E, Idy-Peretti I, de Certaines JD. MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging. 2003; 21(9):989–93.

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