Title | 3D CATBraTS: Channel Attention Transformer for Brain Tumour Semantic Segmentation |
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Authors | El Badaoui, R., Bonmati Coll, E., Psarrou, A. and Villarini, B. |
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Type | Conference paper |
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Abstract | Brain tumour diagnosis is a challenging task yet crucial for planning treatments to stop or slow the growth of a tumour. In the last decade, there has been a dramatic increase in the use of convolutional neural networks (CNN) for their high performance in the automatic segmentation of tumours in medical images. More recently, Vision Transformer (ViT) has become a central focus of medical imaging for its robustness and efficiency when compared to CNNs. In this paper, we propose a novel 3D transformer named 3D CATBraTS for brain tumour semantic segmentation on magnetic resonance images (MRIs) based on the state-of-the-art Swin transformer with a modified CNN-encoder architecture using residual blocks and a channel attention module. The proposed approach is evaluated on the BraTS 2021 dataset and achieved quantitative measures of the mean Dice similarity coefficient (DSC) that surpasses the current state-of-the-art approaches in the validation phase. |
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Keywords | CNN, Transformers, ViT, Semantic Segmentation |
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Year | 2023 |
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Conference | 36th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
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Publisher | IEEE |
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Accepted author manuscript | |
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Publication dates |
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Published | 17 Jul 2023 |
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Journal | 2023 IEEE CBMS Proceedings |
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Digital Object Identifier (DOI) | https://doi.org/10.1109/cbms58004.2023.00267 |
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