Feedback Attention for Cell Image Segmentation
arXiv:2008.06474
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.
arXiv comment14 pages, 4 figures, Accepted by ECCV2020 Workshop "BioImage Computing (BIC)"
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Self-Attention Generative Adversarial Networks
- Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
- Reformer: The Efficient Transformer