Joint sequence learning and cross-modality convolution for 3D biomedical segmentation
Journal
30th IEEE Conference on Computer Vision and Pattern Recognition
Journal Volume
2017-January
Pages
3739-3746
ISBN
9781538604571
Date Issued
2017
Author(s)
Abstract
Deep learning models such as convolutional neural network have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance. However, most of them often adapt a single modality or stack multiple modalities as different input channels. To better leverage the multimodalities, we propose a deep encoder-decoder structure with cross-modality convolution layers to incorporate different modalities of MRI data. In addition, we exploit convolutional LSTM to model a sequence of 2D slices, and jointly learn the multi-modalities and convolutional LSTM in an end-to-end manner. To avoid converging to the certain labels, we adopt a re-weighting scheme and two-phase training to handle the label imbalance. Experimental results on BRATS-2015 [13] show that our method outperforms state-of-the-art biomedical segmentation approaches. ? 2017 IEEE.
SDGs
Type
conference paper
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