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Thyroid nodule focus region generation data enhancement method based on a deep convolutional generative adversarial network

A thyroid nodule and deep convolution technology, applied in the field of artificial intelligence and deep learning, can solve problems such as unstable training, low image resolution, uncontrollable learning features, etc., to achieve credibility and avoid confusion of lesions Effect

Pending Publication Date: 2019-06-11
SUN YAT SEN UNIV
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Problems solved by technology

They used an adversarial method to train the neural network to learn the distribution of training data and realize the real ones, but at the same time, GAN has shortcomings such as unstable training, uncontrollable learning features, and low image resolution.

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  • Thyroid nodule focus region generation data enhancement method based on a deep convolutional generative adversarial network
  • Thyroid nodule focus region generation data enhancement method based on a deep convolutional generative adversarial network
  • Thyroid nodule focus region generation data enhancement method based on a deep convolutional generative adversarial network

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Embodiment Construction

[0026] The following examples are used to further describe the present invention in detail, and should not be used to limit the protection scope of the present invention.

[0027] Such as Figure 1-4 As shown, the embodiment of the present invention provides a data enhancement method based on deep convolution generative adversarial network (DCGAN, Deep Convolution Generative Adversarial Networks) thyroid nodule lesion area generation. Classify according to benign nodules and malignant nodules, and then use the deep convolutional generative confrontation network to generate images of lesion areas, select images that are closer to the real lesion area, and fuse them with thyroid images of normal people to finally generate lesions in Thyroid ultrasound images of different parts of the thyroid, including the following steps:

[0028] S1: After marking the position and type of the nodule with the XML file, remove the label marked by the radiologist on the nodule position on the im...

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Abstract

The invention discloses a thyroid nodule focus region generation data enhancement method based on a deep convolutional generative adversarial network. According to the method, thyroid ultrasound images of a real patient are classified according to benign nodules and malignant nodules; a focus area image is generated by using a deep convolutional generative adversarial network; According to the method for generating image fusion in the lesion area, the image which is generated from the lesion area and is close to the real lesion is selected and fused with the thyroid image of a normal person toachieve the purpose of data enhancement, and the method for generating image fusion in the lesion area improves the quality and diversity of enhanced data, enables the generated image to be close tothe real image to the maximum extent and is more credible.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and deep learning, and in particular relates to a data enhancement method for generating thyroid nodule lesions based on deep convolution generative adversarial networks (DCGAN, Deep Convolution Generative Adversarial Networks). Background technique [0002] In recent years, the incidence of thyroid nodules has shown an increasing trend year by year. Ultrasound imaging technology is currently one of the most commonly used methods for early detection of tumors. Ultrasound is widely used in clinical diagnosis because of its cheapness, no radiation, and low cost. At present, the examination of the properties of thyroid nodules mainly relies on the analysis of ultrasound images. The radiologist summarizes a series of ultrasound image characteristics of thyroid nodules as signs of cancer, including hypoechoic reflectivity, lack of halo, microcalcifications, high hardness, Inflow and shape of no...

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Application Information

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IPC IPC(8): G06T5/50
Inventor 蔡庆玲裴海军何鸿奇梁伟霞周毅
Owner SUN YAT SEN UNIV
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