Rethinking Radiology: An Analysis of Different Approaches to BraTS
This is an incremental analysis for medical imaging researchers, focusing on optimizing segmentation approaches for brain tumors.
The paper compares various deep learning architectures, including UNet, for pixel-wise segmentation of brain tumors on the BRATS dataset, analyzing their performance and discussing potential improvements with newer methods.
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.