Kaul, Chaitanya, Pears, N. E. orcid.org/0000-0001-9513-5634, Dai, Hang et al. (2 more authors) (2021) FocusNet++:Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI):. IEEE International Symposium on Biomedical Imaging, 2021, 13-16 Apr 2021 , FRA
Abstract
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms with group convolutions to create our group attention mechanism, which forms the fundamental building block of our network, FocusNet++. We employ a hybrid loss based on balanced cross entropy, Tversky loss and the adaptive logarithmic loss to enhance the performance along with fast convergence. Our results show that FocusNet++ achieves state-of-the-art results across various benchmark metrics for the ISIC 2018 melanoma segmentation and the cell nuclei segmentation datasets with fewer parameters and FLOPs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Group Ateention, Medical Image Segmentation, Residual Learning |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 26 Aug 2022 10:50 |
Last Modified: | 16 Oct 2024 11:18 |
Published Version: | https://doi.org/10.1109/ISBI48211.2021.9433918 |
Status: | Published |
Identification Number: | 10.1109/ISBI48211.2021.9433918 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190365 |
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