Multi Modal Convolutional Neural Networks forBrain Tumor Segmentation

09/17/2018
by   Mehmet Aygün, et al.
0

In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.

READ FULL TEXT
research
09/17/2018

Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation

In this work, we propose a multi-modal Convolutional Neural Network (CNN...
research
03/09/2022

Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion

Multi-modal magnetic resonance (MR) imaging provides great potential for...
research
10/05/2015

Within-Brain Classification for Brain Tumor Segmentation

Purpose: In this paper, we investigate a framework for interactive brain...
research
06/20/2018

A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

Inspired by the success of Convolutional Neural Networks (CNN), we devel...
research
05/23/2017

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

This paper analyzes the use of 3D Convolutional Neural Networks for brai...
research
12/14/2022

Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?

State-of-the-art brain tumor segmentation is based on deep learning mode...

Please sign up or login with your details

Forgot password? Click here to reset