变压器在脑肿瘤诊断和治疗中的潜在作用

Brain-X Pub Date : 2023-07-13 DOI:10.1002/brx2.23
Yu-Long Lan, Shuang Zou, Bing Qin, Xiangdong Zhu
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引用次数: 1

摘要

脑肿瘤(BT)是显著提高全球人类发病率和死亡率的许多恶性肿瘤之一。神经胶质瘤的早期发现和表征对于有效的预防策略至关重要。目前,变压器这一用于BT诊断和治疗的深度学习模型的使用正引起人们的极大关注。转换器自注意机制自动学习输入数据之间的关联,以进行有效的处理和分析。研究表明,Transformers可以在磁共振成像(MRI)图像的BT分割、基于MRI和组织病理学的脑癌症分级、BT分子表达预测、原发性脑转移部位的分类、体素水平剂量和BT放疗结果预测、协同预测、,以及药物组合的通路去卷积。在这篇综述中,系统地分析了各种算法的可行性、准确性和适用性,并讨论了它们的前景。总之,这篇综述旨在讨论并概述变压器在实时BT检测和治疗中日益增长的应用,表明其广阔的前景和潜力。未来,由于基于Transformer的深度学习技术的不断发展和改进,Transformer有望越来越多地用于BT的诊断和后续治疗。然而,还需要更多的工作来研究它们的特性,用于异常检测、医学图像分类、网络设计开发以及应用于其他医学数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Potential roles of transformers in brain tumor diagnosis and treatment

Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self-attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology-based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel-level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real-time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer-based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.

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