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A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation by Tong, Juncheng; Wang, Chunyan is a scholarly article available to read on EtoBox.

What is A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation about?

The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good processing quality and reliability are the must. Moreover, for wide applications of such systems, a minimization of computation complexity is desirable, which can also result in a minimization of randomness in computation and, consequently, a better performance consistency. To this end, the CNN in the proposed system has a unique structure with 2 distinguished characters. Firstly, the three paths of its feature extraction block are designed to extract, from the multi-modality input, comprehensive feature information of mono-modality, paired-modality and cross-modality data, respectively. Also, it has a particular three-branch classification block to identify the pixels of 4 classes. Each branch is trained separately so that the parameters are updated specifically with the corresponding ground truth data of a target tumor areas. The convolution layers o

Author

Tong, Juncheng; Wang, Chunyan

Published

2022

Language

EN