Mr. Yintao Tai Profile

Yintao Tai

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Proceedings Article | 1 December 2021 Paper

Brain tumor segmentation with attention-based U-Net

Tuofu Li, Javin Jia Liu, Yintao Tai, Yuxuan Tian

Proceedings Volume 12079, 120790N (2021) https://doi.org/10.1117/12.2623112

KEYWORDS: Image segmentation, Tumors, Brain, Magnetic resonance imaging, Data modeling, Performance modeling, Image classification, Neuroimaging, Diagnostics, Medicine

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Brain tumors are a hazardous type of tumor, and they build pressure inside the skull when they grow, which can potentially cause brain damage or even death. Attention mechanisms are widely adopted in state-of-the-art deep learning architectures for computer vision and neural translation tasks since they enhance networks' ability to capture spatial and channel-wise relationships. We offer an attention-based image segmentation model that outlines the brain tumors in Magnetic Resonance Imaging (MRI) scans if present. In the paper, we mainly focus on integrating Squeeze-and-Excitation Block and CBAM into the commonly used segmentation model, U-Net, to resolve the problem of concatenating unnecessary information into the decoder blocks and attempt to locate the tumor boundaries. Our research clearly shows the application of the attention mechanism in U-Net, incorporates the Squeeze-and-Excitation with CBAM, and improves the performance in the brain tumor segmentation task. The model is delivered on an app with additional text to speech and chatbot features provided.

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