FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware adva...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2023-11, Vol.34 (11), p.9375-9388
Main Authors: Tomar, Nikhil Kumar, Jha, Debesh, Riegler, Michael A., Johansen, Havard D., Johansen, Dag, Rittscher, Jens, Halvorsen, Pal, Ali, Sharib
Format: Article
Language:English
Subjects:
Biological system modeling
Biomedical imaging
Cell nuclei
colon polyps
Computer architecture
Datasets
Deep learning
Feature maps
Feedback
feedback attention
Image analysis
Image processing
Image segmentation
Imaging
Iterative methods
lung segmentation
medical image segmentation
Medical imaging
retinal vessels
Semantics
skin lesion
Source code
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet .
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3159394