Feedback Attention for Cell Image Segmentation

TL;DR

This paper introduces a Feedback Attention mechanism inspired by human brain processing to improve cell image segmentation, demonstrating better results than traditional feedforward models.

Contribution

It proposes a novel Feedback Attention mechanism that incorporates feedback processing in neural networks for cell segmentation, inspired by human brain functions.

Findings

Feedback Attention improves segmentation accuracy.

U-Net with Feedback Attention outperforms conventional models.

Feedback mechanism enhances feature utilization.

Abstract

In this paper, we address cell image segmentation task by Feedback Attention mechanism like feedback processing. Unlike conventional neural network models of feedforward processing, we focused on the feedback processing in human brain and assumed that the network learns like a human by connecting feature maps from deep layers to shallow layers. We propose some Feedback Attentions which imitate human brain and feeds back the feature maps of output layer to close layer to the input. U-Net with Feedback Attention showed better result than the conventional methods using only feedforward processing.