AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

TL;DR

This paper introduces AGD-Autoencoder, a novel attention gated deep convolutional autoencoder that improves brain tumor segmentation accuracy in fMRI images by integrating edge detection and attention mechanisms with minimal computational cost.

Contribution

The paper presents a new attention gate model that combines edge detection and attention mechanisms within CNNs for improved brain tumor segmentation.

Findings

Achieved an IOU of 0.78 on brain tumor segmentation.

Significantly increased sensitivity scores with minimal computational overhead.

Eliminated the need for explicit tissue localization and classification.

Abstract

Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images. This feature enables us to eliminate the necessity of having to explicitly point towards the damaged area(external tissue localization) and classify(classification) as per classical computer vision techniques. AGs can easily be integrated within the deep convolutional neural networks(CNNs). Minimal computional overhead is required while the AGs increase the sensitivity scores significantly. We show that the edge detector along with an attention gated mechanism provide a sufficient enough method for brain segmentation reaching an IOU of 0.78