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

arXiv: https://arxiv.org/abs/2107.03323 · HTML: https://mlsanity.com/paper/2107.03323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm](https://mlsanity.com/paper/1906.03720/) (2019)

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

- [Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation](https://mlsanity.com/paper/2305.07850/) (2023)

## Abstract (arXiv)

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