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...
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Published in | IEEE transaction on neural networks and learning systems Vol. 34; no. 11; pp. 9375 - 9388 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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United States
IEEE
01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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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) 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 . |
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AbstractList | 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 . |
Author | Halvorsen, Pal Jha, Debesh Johansen, Dag Ali, Sharib Tomar, Nikhil Kumar Johansen, Havard D. Rittscher, Jens Riegler, Michael A. |
Author_xml | – sequence: 1 givenname: Nikhil Kumar surname: Tomar fullname: Tomar, Nikhil Kumar organization: SimulaMet, Oslo, Norway – sequence: 2 givenname: Debesh orcidid: 0000-0002-8078-6730 surname: Jha fullname: Jha, Debesh email: debesh@simula.no organization: SimulaMet, Oslo, Norway – sequence: 3 givenname: Michael A. orcidid: 0000-0002-3153-2064 surname: Riegler fullname: Riegler, Michael A. organization: SimulaMet, Oslo, Norway – sequence: 4 givenname: Havard D. orcidid: 0000-0002-1637-7262 surname: Johansen fullname: Johansen, Havard D. organization: Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway – sequence: 5 givenname: Dag orcidid: 0000-0001-7067-6477 surname: Johansen fullname: Johansen, Dag organization: Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway – sequence: 6 givenname: Jens orcidid: 0000-0002-8528-8298 surname: Rittscher fullname: Rittscher, Jens organization: Department of Engineering Science and the Li Ka Shing Centre for Health Information and Discovery, Big Data Institute, University of Oxford, Oxford, U.K – sequence: 7 givenname: Pal orcidid: 0000-0003-2073-7029 surname: Halvorsen fullname: Halvorsen, Pal organization: SimulaMet, Oslo, Norway – sequence: 8 givenname: Sharib orcidid: 0000-0003-1313-3542 surname: Ali fullname: Ali, Sharib email: sharib.ali@eng.ox.ac.uk organization: Department of Engineering Science, University of Oxford, Oxford, U.K |
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SubjectTerms | 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 |
Title | FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation |
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