# 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

arXiv: https://arxiv.org/abs/1705.08236 · HTML: https://mlsanity.com/paper/1705.08236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 4 of 4)

- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data](https://mlsanity.com/paper/2112.01034/) (2021)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Triplet Contrastive Learning for Brain Tumor Classification](https://mlsanity.com/paper/2108.03611/) (2021)

## Abstract (arXiv)

This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.