@inproceedings{1571433795f94f8aa4a11cf7e0192824,
title = "Deep learning based multimodal brain tumor diagnosis",
abstract = "Brain tumor segmentation plays an important role in the disease diagnosis. In this paper, we proposed deep learning frameworks, i.e. MvNet and SPNet, to address the challenges of multimodal brain tumor segmentation. The proposed multi-view deep learning framework (MvNet) uses three multi-branch fully-convolutional residual networks (Mb-FCRN) to segment multimodal brain images from different view-point, i.e. slices along x, y, z axis. The three sub-networks produce independent segmentation results and vote for the final outcome. The SPNet is a CNN-based framework developed to predict the survival time of patients. The proposed deep learning frameworks was evaluated on BraTS 17 validation set and achieved competing results for tumor segmentation While Dice scores of 0.88, 0.75 0.71 were achieved for whole tumor, enhancing tumor and tumor core, respectively, an accuracy of 0.55 was obtained for survival prediction.",
keywords = "Deep learning, Multi-view, Survival prediction, Tumor segmentation",
author = "Yuexiang Li and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 ; Conference date: 14-09-2017 Through 14-09-2017",
year = "2018",
doi = "10.1007/978-3-319-75238-9_13",
language = "English",
isbn = "9783319752372",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "149--158",
editor = "Bjoern Menze and Alessandro Crimi and Hugo Kuijf and Mauricio Reyes and Spyridon Bakas",
booktitle = "Brainlesion",
address = "Germany",
}
