基于cnn的青光眼检测中固有图像特征的可视化和理解

Dhaval Vaghjiani, Sajib Saha, Yann Connan, Shaun Frost, Y. Kanagasingam
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引用次数: 5

摘要

基于卷积神经网络(CNN)的方法在青光眼检测中取得了最先进的性能。尽管如此,这些方法经常受到批评,因为它们没有提供机会来理解如何做出分类决策。在本文中,我们开发了一种创新的可视化策略,可以理解不同CNN层中有助于青光眼检测的固有图像特征。我们还开发了一套可解释的概念,以更好地理解疾病检测过程中涉及的贡献图像特征。在公开可用的青光眼数据集上进行了广泛的实验。结果显示,视杯对青光眼检测的影响最大(IoU评分为0.18),其次是神经视网膜缘(NR), IoU评分为0.17。照片中血管的IoU总分为0.16,在疾病检测中也发挥了相当大的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing and Understanding Inherent Image Features in CNN-based Glaucoma Detection
Convolutional neural network (CNN)-based methods have achieved state-of-the-art performance in glaucoma detection. Despite this, these methods are often criticized for offering no opportunity to understand how classification decisions are made. In this paper, we develop an innovative visualization strategy that allows the inherent image features contributing to glaucoma detection at different CNN layers to be understood. We also develop a set of interpretable notions to better comprehend the contributing image features involved in the disease detection process. Extensive experiments are conducted on publicly available glaucoma datasets. Results show that the optic cup is the most influential ocular component for glaucoma detection (overall Intersection over Union (IoU) score of 0.18), followed by the neuro-retinal rim (NR) with IoU score 0.17. With an overall IoU score of 0.16 vessels in the photograph also play a considerable role in the disease detection.
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