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Automatic Generation of Polyp Image using Depth Map for Endoscope Dataset

机译:使用深度映射为内窥镜数据集自动生成息肉图像

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In recent years, opportunities for diagnosis using endoscopy aiming a less invasive treatment are increasing following the disease rate of colorectal cancer. Computer-aided diagnosis has been developed based on deep learning methodology, it aiming to improve the accuracy of diagnosis and support immature medical doctors. To satisfy the learning dataset, this paper proposes a data augmentation methodology where automatic image generation of polyp images using Pix2Pix and depth map obtained from the original image. The problem of lack of the learning dataset of polyp images can be solved by the proposed approach and the effectiveness of the generated data was confirmed by the quantitative evaluation with the improved performance of SSD (Single Shot Multibox Detector) in the experiments.
机译:近年来,在结肠直肠癌疾病率后,使用内窥镜检查的诊断机会旨在较少的侵入性治疗。 基于深度学习方法开发了计算机辅助诊断,旨在提高诊断的准确性和支持未成熟医生的准确性。 为了满足学习数据集,本文提出了一种数据增强方法,其中使用从原始图像获得的PIX2PIX和深度图的息肉图像的自动图像生成。 通过所提出的方法可以解决缺乏息肉图像的学习数据集的问题,并且通过在实验中的SSD(单次射击多杆探测器)的改善性能的定量评估来确认所生成数据的有效性。

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