# Rethinking Radiology: An Analysis of Different Approaches to BraTS

arXiv: https://arxiv.org/abs/1806.03981 · HTML: https://mlsanity.com/paper/1806.03981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI](https://mlsanity.com/paper/2409.17273/) (2024)

## Abstract (arXiv)

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.