FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Simula Metropolitan Center for Digital Engineering · UiT The Arctic University of Norway · +4 more institutions
Abstract
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)…
Citation impact
- FWCI
- 30.82
- Percentile
- 100%
- References
- 78
Authors
8- NKNikhil Kumar TomarCorresponding
Simula Metropolitan Center for Digital Engineering
- DJDebesh Jha
Simula Metropolitan Center for Digital Engineering
- MAMichael A. Riegler
Simula Metropolitan Center for Digital Engineering
- HDHåvard D. Johansen
UiT The Arctic University of Norway
- DJDag Johansen
UiT The Arctic University of Norway
Topics & keywords
- Computer science
- Leverage (statistics)
- Segmentation
- Deep learning
- Feature (linguistics)
- Artificial intelligence
- Source code
- Code (set theory)