The Fully Convolutional Transformer for Medical Image Segmentation

Tragakis, A., Kaul, C., Murray-Smith, R. and Husmeier, D. (2023) The Fully Convolutional Transformer for Medical Image Segmentation. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2023), Waikoloa, HI, USA, 03-07 Jan 2023, pp. 1022-1031. ISBN 9781665493468 (doi: 10.1109/WACV56688.2023.00365)

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We propose a novel transformer, capable of segmenting medical images of varying modalities. Challenges posed by the fine-grained nature of medical image analysis mean that the adaptation of the transformer for their analysis is still at nascent stages. The overwhelming success of the UNet lay in its ability to appreciate the fine-grained nature of the segmentation task, an ability which existing transformer based models do not currently posses. To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs. The FCT is the first fully convolutional Transformer model in medical imaging literature. It processes its input in two stages, where first, it learns to extract long range semantic dependencies from the input image, and then learns to capture hierarchical global attributes from the features. FCT is compact, accurate and robust. Our results show that it outperforms all existing transformer architectures by large margins across multiple medical image segmentation datasets of varying data modalities without the need for any pre-training. FCT outperforms its immediate competitor on the ACDC dataset by 1.3%, on the Synapse dataset by 4.4%, on the Spleen dataset by 1.2% and on ISIC 2017 dataset by 1.1% on the dice metric, with up to five times fewer parameters. On the ACDC Post-2017-MICCAI-Challenge online test set, our model sets a new state-of-the-art on unseen MRI test cases out-performing large ensemble models as well as nnUNet with considerably fewer parameters. Our code, environments and models will be available via GitHub † .

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Husmeier, Professor Dirk and Tragakis, Athanasios and Kaul, Dr Chaitanya
Authors: Tragakis, A., Kaul, C., Murray-Smith, R., and Husmeier, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2023 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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