针对肝脏瘤诊断的强大的多模式超声波分类:一种对模式缺失的生成方法
Jiali Guo1, Rui Bu2, Wanting Shen3
1Yunnan University of Finance and Economics, Kunming, Yunnan, China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
Computer methods and programs in biomedicine
|April 6, 2025
概括
生成模型重建缺失的医疗图像用于肝癌诊断,保持高准确度. 当成像数据不完整时,这种方法可以增强多式联运分类模型.
科学领域:
- 医学图像分析 医学图像分析
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 多模式医学成像提高了诊断准确度,但在临床实践中缺少数据限制了这一点.
- 由于成像数据集不完整,肝脏瘤诊断往往面临挑战.
- 开发能够处理缺失模式的强大模型对于临床应用至关重要.
研究的目的:
- 提出一种基于生成模型的方法,用于肝脏瘤诊断中的跨模式重建和分类.
- 为应对多式联络诊断框架中缺少成像模式的挑战.
- 评估重建图像在维持分类性能方面的有效性.
主要方法:
- 开发了一种使用DenseNet和EfficientNet进行特征提取的多模式分类框架 (Model_D).
- 使用特征级融合与分类的模式权重.
- 利用生成模型 (U-GAT-IT,MSA-GAN) 进行缺失的B模式超声波或彩色多普勒流成像 (CDFI) 数据的交叉模式重建.
- 集成的重建图像作为Model_D的补充数据.
主要成果:
- 全模式的Model_D实现了88.57%的准确性,0.95 AUC用于肝脏瘤分类.
- 重建的图像 (PSNR > 20,SSIM > 0.7) 适合补充缺失的模式.
- 使用补充图像的分类精度达到87.10% (CDFI) 和86.43% (B模式),AUC为0.92和0.95.95.
- 生成性重建尽管缺少模式,但仍然保持了高分类性能.
结论:
- 基于生成模型的模式重建显著提高了用于肝脏瘤诊断的多模式分类的稳定性.
- 这种方法提高了多式模式模型的临床适用性,通过确保高精度,即使缺少数据.
- 未来的工作重点是改进重建技术,以获得更广泛的临床概括.
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