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About Multimodal Brain MRI Tumor Segmentation Via Convolutional Neural Network PDF
This document presents three convolutional neural network architectures for brain tumor segmentation from multimodal MRI images. It discusses previous work applying deep learning to medical image segmentation and analyses challenges like small datasets and heterogeneity. The architectures include a voxel-wise CNN, a patch-wise fully convolutional CNN, and a full-image fully convolutional CNN. Results on the BraTS dataset show the patch-wise approach achieves a mean dice score of 0.86 for tumor segmentation. Transfer learning is also explored by applying models pretrained on BraTS to the Rembrandt dataset.
Detailed Information
| Author: | ['Sharif Ahamed'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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