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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Generative caption for diabetic retinopathy images
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Generative caption for diabetic retinopathy images

机译：糖尿病视网膜病变图像的生成字幕

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摘要

For a long time, the detection of diabetic retinopathy has always been a great challenge. People want to find a fast and effective computer-aided treatment to diagnose the disease. In recent years, the rapid development of the deep learning makes it gradually become an effective technique for the analysis of medical images. In this paper, we propose a method to deal with diabetic retinopathy images with generative caption technique of images to generate a simple sequence to explain the abnormal contents in fundus images. The generative technique of images is a generative model based on a deep recurrent architecture that combines convolution neural network (CNN) which is currently state-of-the-art for object recognition and detection with long-short-term-memory (LSTM) which is applied with great success to machine translation and sequence generation, and that can be used to generate natural sentences describing an image. The target of the model in training is to maximize the likelihood of the target description sentence given from the training images. The model built on dataset DIARETDB0, DIARETDB1 and Messidor can achieve good performance and generate fluent sequences. In addition, the experimental results show that the accuracy of diagnosis for individual abnormal discoveries is up to 88.53% and the diagnosis accuracy is more than 90%.
机译：长期以来，糖尿病性视网膜病变的检测一直是一个巨大的挑战。人们希望找到一种快速有效的计算机辅助治疗方法来诊断这种疾病。近年来，深度学习的迅速发展使其逐渐成为一种有效的医学图像分析技术。在本文中，我们提出了一种使用图像的生成字幕技术处理糖尿病性视网膜病变图像的方法，以生成一个简单的序列来解释眼底图像中的异常内容。图像的生成技术是基于深度递归体系结构的生成模型，该体系结构结合了当前用于对象识别和检测的最新技术卷积神经网络（CNN）和长短期记忆（LSTM）。被成功应用于机器翻译和序列生成，并且可用于生成描述图像的自然句子。训练中模型的目标是使从训练图像给出的目标描述语句的可能性最大化。建立在数据集DIARETDB0，DIARETDB1和Messidor上的模型可以实现良好的性能并生成流畅的序列。实验结果表明，单个异常发现的诊断准确率高达88.53％，诊断准确率超过90％。

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