Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas-Reference-Cited by-同舟云学术

Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas

Published:2019 Issue: Volume: Page:369-379
ISSN:0302-9743
Container-title:Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Author:

Baid Ujjwal,Talbar Sanjay,Rane Swapnil,Gupta Sudeep,Thakur Meenakshi H.,Moiyadi Aliasgar,Thakur Siddhesh,Mahajan Abhishek

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-11726-9_33

Reference15 articles.

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3. Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 1–13 (2017)

4. Kickingereder, P., et al.: Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiol. 280(3), 880–889 (2016)

5. Konstantinos, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

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