CoVi-Net:用于视网膜血管细分的混合卷积和视觉转换器神经网络
Minshan Jiang1, Yongfei Zhu1, Xuedian Zhang1
1Shanghai Key Laboratory of Contemporary Optics System, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Computers in biology and medicine
|January 31, 2024
概括
本研究介绍了CoVi-Net,这是一种混合深度学习模型,用于增强视网膜血管细分. CoVi-Net 通过有效地将 fundus 图像中的本地和全球特征融合在一起,提高了诊断眼部病理的准确性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 准确的视网膜血管细分对于诊断眼部疾病至关重要.
- 现有的方法难以从基金图像中整合全球和本地特征,限制了诊断能力.
研究的目的:
- 开发一个先进的混合深度学习网络,CoVi-Net,以改善视网膜血管细分.
- 解决现有细分技术中特征融合和同时捕获多尺度特征的局限性.
主要方法:
- 推出了CoVi-Net,这是一个混合网络,结合了卷积神经网络和视觉转换器.
- 开发了新的模块:局部和全球特征聚合 (LGFA),双向加权特征融合 (BWF) 和自适应侧面特征融合 (ALFF).
- 集成的水平和垂直连接,用于增强的多尺度特征融合.
主要成果:
- 在DRIVE,CHASEDB1和STARE数据集上,CoVi-Net实现了卓越的性能,超过了最先进的方法.
- 实现了高的全球精度 (高达0.9761) 和曲线下的面积 (高达0.9915).
- 废弃性研究证实了单个模块的有效性,并证明了对损伤细分的适应性.
结论:
- CoVi-Net为准确的视网膜血管细分提供了一个有希望的方法.
- 该模型的高级功能融合策略增强了视网膜血管疾病的诊断.
- 在眼科中,CoVi-Net显示出临床应用的潜力.
相关概念视频
01:24Vision
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
01:26Visual System
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Once through the pupil, the light passes through the lens, a...