ConvTNet融合:用于多类分类,多式特征融合和组织异质性处理的强大的变压器-CNN框架
Tariq Mahmood1, Tanzila Saba2, Amjad Rehman2
1Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, 11586, Kingdom of Saudi Arabia; Department of Information Sciences, University of Education, Vehari Campus 61100, Vehari, Pakistan.
概括
这项研究介绍了ConvTNet,一种用于增强脏CT图像细分的混合深度学习模型. 通过精确划分瘤和周围组织,ConvTNet提高了癌的诊断准确性.
科学领域:
- 医学成像
- 人工智能
- 癌症学
背景情况:
- 医学成像对于诊断器官结构和功能至关重要.
- 自动化图像细分有助于诊断和治疗计划, 但面临着诸如阶级不平衡和复杂组织边界等挑战.
- 精确细分CT图像对于有效治疗癌至关重要.
研究的目的:
- 开发和评估一个新的混合模型ConvTNet,该模型结合了变压器和卷积神经网络 (CNN) 的功能,以改善部CT图像的细分.
- 解决细分CT图像的挑战,包括类不平衡和模糊的组织边界.
- 通过优质的图像细分来提高癌诊断的准确性.
主要方法:
- 开发了ConvTNet,这是一个融合了Transformer和CNN架构的混合模型.
- 纳入了专注于关键区域的KC模块和用于多尺度特征融合的Mix-KFCA模块.
- 实施了创新的预处理策略:降低噪音,增强数据和图像规范化.
- 通过微调五个预训练模型来提高特征提取能力,利用转移学习.
主要成果:
- 在多标签分类和病变细分方面,ConvTNet表现出色.
- 获得的高度指标:AUC为0. 9970,灵敏度为0. 9942,子相似系数 (DSC) 为0. 9533,准确度为0. 9921.
- 该模型有效地从周围结构中划分健康的组织,并处理噪音边界.
结论:
- ConvTNet显著提高了部CT图像分段的准确性.
- 混合模型的性能验证了其用于精确诊断癌的有效性.
- 对于医疗成像中的复杂细分任务,ConvTNet提供了一个强大的解决方案.
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