Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2209.07704v1
• Date: Fri, 16 Sep 2022 03:55:48 GMT
• Title: Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation
• Authors: Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary Egan, Mehrtash Harandi
• Abstract summary: We propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%.
• Score: 28.650980942429726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local distributional smoothness (LDS) during model training inspired by virtual adversarial training (VAT) to make the model robust. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%; Hausdorff Distance (95%) of 14.81 mm, 3.93 mm, 11.18 mm for the enhancing tumor, whole tumor, and tumor core, respectively. Overall, the experimental results verify our method's effectiveness by yielding better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available : https://github.com/himashi92/vizviva_fets_2022

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