在CT图像中进行肺炎,肺结节和肺结核的自动细分的一般方法
1Department of Library, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
iScience
|August 3, 2023
概括
一个新的级联式双重注意网络有效地细分了各种肺病变,如肺炎和CT扫描中的结节. 这种方法在有限的数据中保持准确性,并减少模型遗忘,提高放射性效率.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 在CT图像中精确细分肺病变 (结节,肺炎,结核病) 对于高效的放射学至关重要.
- 像生成对抗网络 (GAN) 这样的现有方法受到有限的数据和歧视者遗忘的影响.
- 基于形态学的传统方法缺乏对细分不同肺病理的普遍性.
研究的目的:
- 提出CT图像中多种肺病变类型的一般细分方法.
- 为了解决GAN和传统方法在肺损伤细分方面的局限性.
- 在放射学工作流程中提高肺病变检测和细分的效率和准确性.
主要方法:
- 开发一个级联式的双重注意网络,其中包含一个上下文感知的金字塔特征提取模块.
- 实现自我监督的旋转损失,以减轻在生成对抗网络中的歧视者遗忘.
- 对肺炎,肺结节和结核的多中心数据集的验证.
主要成果:
- 达到了70.92% (肺炎),73.55% (肺结节) 和68.52% (肺结核) 的子系数.
- 在小型训练样本大小 (p > 0.10) 时,准确度没有显著下降.
- 显著减少了与自主监督旋转损失的区分器循环训练 (p < 0.01).
结论:
- 拟议的级联双重注意网络在CT图像中对细分多种肺病变显示出希望.
- 自主监督的旋转损失有效地减轻了区分器的遗忘,提高了模型的稳定性.
- 这种方法提供了一个潜在的解决方案,可以提高肺病理的放射性诊断效率.
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