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首页> 外文会议>International Conference on Artificial Neural Networks >Unsupervised Change Detection Using Joint Autoencoders for Age-Related Macular Degeneration Progression
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Unsupervised Change Detection Using Joint Autoencoders for Age-Related Macular Degeneration Progression

机译：使用联合自动化器进行年龄相关黄斑变性进展的无监督变化检测

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Age-Related Macular Degeneration (ARMD) is an eye disease that has been an important research field for two decades now. Researchers have been mostly interested in studying the evolution of lesions that slowly causes patients to go blind. Many techniques ranging from manual annotation to mathematical models of the disease evolution bring interesting leads to explore. However, artificial intelligence for ARMD image analysis has become one of the main research focus to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced doctors. Within this context, in this paper, we propose a neural network architecture for change detection in eye fundus images to highlight the evolution of the disease. The proposed method is fully unsupervised, and is based on fully convolutional joint autoencoders. Our algorithm has been applied to several pairs of images from eye fundus images time series of ARMD patients, and has shown to be more effective than most state-of-the-art change detection methods, including non-neural network based algorithms that are usually used to follow the evolution of the disease.
机译：年龄相关的黄斑变性（ARMAD）是一种眼病，这是二十年来的重要研究领域。研究人员大多数人对研究病变的演变感兴趣，慢慢导致患者失明。许多技术从手动注释到疾病演变的数学模型带来了有趣的导线探索。然而，武装图像分析的人工智能已成为研究疾病进展的主要研究重点之一，因为即使对于经验丰富的医生也已经证明其演化的准确手动注释已经难以使用传统方法。在这方面，在本文中，我们提出了一种神经网络架构，用于改变眼底图像的变化检测，以突出疾病的演变。该方法完全无监督，基于完全卷积的联合自动化器。我们的算法已被应用到多对从眼底图像的时间序列ARMD患者的图像，并且已经显示出比状态的最先进的最变化的检测方法，包括基于非神经网络的算法，通常是更有效的用来遵循疾病的演变。

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