Focusnet++: attentive aggregated transformations for efficient and accurate medical image segmentation

Kaul, C., Pears, N., Dai, H., Murray-Smith, R. and Manandhar, S. (2021) Focusnet++: attentive aggregated transformations for efficient and accurate medical image segmentation. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 13-16 April 2021, pp. 1042-1046. ISBN 9781665412469 (doi: 10.1109/ISBI48211.2021.9433918)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick and Dai, Dr Hang and Kaul, Dr Chaitanya
• Authors: Kaul, C., Pears, N., Dai, H., Murray-Smith, R., and Manandhar, S.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 1945-8452
• ISBN: 9781665412469
• Published Online: 25 May 2021
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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