论文标题
白内障分类的机器学习和眼科成像方式的分级：调查
Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey
论文作者
论文摘要
白内障是全球视觉障碍和失明的主要原因。多年来，研究人员在开发自动白内障分类和分级的最新机器学习技术方面取得了重大进展，旨在防止及早白内障并提高临床医生的诊断效率。这项调查提供了对基于眼科图像的白内障分类/分级的机器学习技术最新进展的全面调查。我们从两个研究方向总结了现有文献：常规机器学习方法和深度学习方法。这项调查还提供了有关功绩和局限性现有作品的见解。此外，我们讨论了基于机器学习技术的自动白内障分类/分级的几个挑战，并为未来的研究提出了可能的解决方案。
Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians' diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.
