Article: Focal cross transformer: multi-view brain tumor segmentation model based on cross window and focal self-attention.
2023 Volume 17, Page(s) 1192867
Abstract: Introduction: Recently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the ... ...
| Abstract | Introduction: Recently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the acquisition of short-term and long-term visual dependencies through self-attention mechanisms; this technology can efficiently learn global and remote semantic information interactions. However, there are certain challenges associated with the use of Transformers. The computational cost of the global self-attention mechanism increases quadratically, thus hindering the application of Transformers for high-resolution images. Methods: In view of this, this paper proposes a multi-view brain tumor segmentation model based on cross windows and focal self-attention which represents a novel mechanism to enlarge the receptive field by parallel cross windows and improve global dependence by using local fine-grained and global coarse-grained interactions. First, the receiving field is increased by parallelizing the self-attention of horizontal and vertical fringes in the cross window, thus achieving strong modeling capability while limiting the computational cost. Second, the focus on self-attention with regards to local fine-grained and global coarse-grained interactions enables the model to capture short-term and long-term visual dependencies in an efficient manner. Results: Finally, the performance of the model on Brats2021 verification set is as follows: dice Similarity Score of 87.28, 87.35 and 93.28%; Hausdorff Distance (95%) of 4.58 mm, 5.26 mm, 3.78 mm for the enhancing tumor, tumor core and whole tumor, respectively. Discussion: In summary, the model proposed in this paper has achieved excellent performance while limiting the computational cost. |
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| Language | English |
| Publishing date | 2023-05-12 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2411902-7 |
| ISSN | 1662-453X ; 1662-4548 |
| ISSN (online) | 1662-453X |
| ISSN | 1662-4548 |
| DOI | 10.3389/fnins.2023.1192867 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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