多种风格的空间注意力模块用于AS-OCT图像中的皮质白内障分类,具有监督的对比学习.
Zunjie Xiao1, Xiaoqing Zhang2, Bofang Zheng1
1Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Computer methods and programs in biomedicine
|December 9, 2023
概括
这项研究引入了一种新的深度学习方法,MSSANet,使用前段光学连贯断层扫描 (AS-OCT) 图像准确地分类皮质白内障 (CC). 这种新的方法提高了早期干预的诊断准确性和可解释性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 皮质白内障 (CC) 的精确分类对于及时干预和手术规划至关重要.
- 前段光学连贯性断层扫描 (AS-OCT) 对白内障诊断有希望,但由于复杂的不透明度分布,CC的自动分类仍然具有挑战性.
研究的目的:
- 探索CC不透明度分布特征作为临床先验,以提高深 convolutional神经网络 (CNN) 在CC分类中的性能.
- 增强基于AS-OCT的CC分类的CNN的表示能力和可解释性.
主要方法:
- 提出了一种新的多样空间注意模块 (MSSA),用于根据临床背景重新校准CNN特征图.
- MSSA集成了集团智能风格聚合 (GSP),局部转换 (LT) 和风格特征再校准 (SFR) 以进行精细的特征地图再校准.
- 该MSSA模块的设计旨在轻松集成到现有的CNN架构,以最小的计算开销.
主要成果:
- 在CASIA2 AS-OCT和两个公共眼科数据集上进行了广泛的实验,证明了MSSA在最先进的注意力方法上的优势.
- 视觉化分析和消去研究证实了MSSA在决策过程中的增强可解释性.
结论:
- 拟议的MSSANet有效地利用CC的不透明度分布特征,以提高CNN的表现力和可解释性.
- 这种方法显示了改善皮质白内障早期临床诊断的巨大潜力.
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