Rethinking Radiology: An Analysis of Different Approaches to BraTS
arXiv:1806.03981
Abstract
This paper discusses the deep learning architectures currently used for pixel-wise segmentation of primary and secondary glioblastomas and low-grade gliomas. We implement various models such as the popular UNet architecture and compare the performance of these implementations on the BRATS dataset. This paper will explore the different approaches and combinations, offering an in depth discussion of how they perform and how we may improve upon them using more recent advancements in deep learning architectures.
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Deep Residual Learning for Image Recognition
- Road Extraction by Deep Residual U-Net
- Striving for Simplicity: The All Convolutional Net
- Highway Networks
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- Aggregated Residual Transformations for Deep Neural Networks