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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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使用基于NFNet的深度学习模型与定制的混合注意力机制来分类青光眼.

Sandeep Angara1, Loc Tran1, Jongwoo Kim1

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.

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概括

一种新的混合注意力机制使用无正常化ResNet架构改进了对眼的检测. 这种方法提高了通过 fundus 图像识别青光眼的准确性,这对于预防不可逆转的失明至关重要.

关键词:
道的注意力 道的注意力深度学习是一种深度学习.玻璃眼 glaucoma 玻璃眼 玻璃眼 玻璃眼 玻璃眼混合注意力 混合注意力没有正常化的ResNetResNet.空间上的注意力

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科学领域:

  • 眼科医生 眼科 眼科
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 玻璃眼是全球不可逆转失明的主要原因.
  • 早期检测至关重要,因为玻璃眼常常缺乏症状,并且无声地进展.
  • 周围视力丧失很常见,直到中央视力受到影响之前,往往没有被注意到.

研究的目的:

  • 开发和评估一种混合注意力机制,以提高青光眼的检测能力.
  • 评估与注意模块结合的无规范化ResNet架构的性能.
  • 为了提高自动化玻璃眼瘤诊断的准确性和效率,从 fundus 图像.

主要方法:

  • 提出了一种混合注意力机制,用于重新校准特征地图以进行青光眼的分类.
  • 使用了没有规范化的ResNet (NF-ResNet) 架构 (NF-ResNet-26, -50, -101).
  • 在三个公共数据集 (LAG,EyePACS,BrG) 上评估该模型,以区分正常与眼底图像.

主要成果:

  • 混合注意模块与NF-ResNet架构显著优于最先进的ResNet变体.
  • 带有注意模块的NF-ResNet-50实现了高精度:0.9394 (LAG),0.9117 (EyePACS),0.9020 (BrG) 的高精度.
  • 在组合数据集上,该模型达到0.9193准确度,0.9182灵敏度和0.9202特异性.

结论:

  • 提出的注意力模块在青光眼检测方面表现出了卓越的表现.
  • 混合注意模块与无规范化架构相结合,是一种高度竞争力的分类模型.
  • 这种方法为早期和准确的青光眼诊断提供了一个有希望的工具,有助于维护视力.