# Feedback Attention for Cell Image Segmentation

arXiv: https://arxiv.org/abs/2008.06474 · HTML: https://mlsanity.com/paper/2008.06474/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture](https://mlsanity.com/paper/2106.10118/) (2021)
- [Accuracy Improvement of Cell Image Segmentation Using Feedback Former](https://mlsanity.com/paper/2408.12974/) (2024)

## Abstract (arXiv)

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.