Attention-based deep learning approaches in brain tumor image analysis: a mini review

Introduction: Accurate diagnosis is crucial for brain tumors, given their low survival rates and high treatment costs. However, traditional methods relying on manual interpretation of medical images are time-consuming and prone to errors. Attention-based deep learning, utilizing deep neural networks to selectively focus on relevant features, offers a promising solution.

Material and Methods: This paper presents an overview of recent advancements in attention-based deep learning for brain tumor image analysis. While the reviewed models have demonstrated respectable performance across different datasets, they have yet to achieve state-of-the-art results.

Results: Advanced techniques, including super-resolution image reconstruction, multi-swin-transformer blocks, and spatial group-wise enhanced attention blocks, have shown improved segmentation network performance. Integration of graph attention, swin-transformer, and gradient awareness minimization with positional attention convolution blocks, self-attention blocks, and intermittent fully connected layers has considerably enhanced the efficiency of classification networks.

Conclusion: While attention-based deep learning has shown improvements in performance, challenges persist. These challenges include the requirement for large datasets, resource limitations, accurate segmentation of irregularly shaped tumors, and high computational demands. Future studies should address these challenges to further enhance the efficiency of brain tumor diagnoses in real-world settings.