CT image classification method, system and device based on semi-supervised deep learning
A CT image and deep learning technology, applied in the field of image processing, can solve the problem of unrecognizable unlabeled CT images, etc., and achieve the effect of improving classification accuracy, high classification accuracy, and improving accuracy and stability
Active Publication Date: 2021-08-31
INST OF AUTOMATION CHINESE ACAD OF SCI
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[0004] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem that the existing supervised learning method cannot identify unlabeled CT images, the first aspect of the present invention proposes a CT image classification method based on semi-supervised deep learning. include:
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[0039] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than Full examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0040] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are sho...
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The invention belongs to the technical field of image processing, and in particular relates to a CT image classification method, system, and device based on semi-supervised deep learning, aiming to solve the problem that the existing supervised learning method cannot identify unlabeled CT images. The method of the present invention includes: taking the three-dimensional region of interest of the CT image to be classified as the first region of interest, and selecting a three-dimensional region of a first preset size as the second region of interest according to the center point coordinates of the first region of interest; The cubic spline difference algorithm scales the first region of interest to the second preset size, and normalizes the second region of interest and the zoomed first region of interest; according to the normalized first sense For the region of interest and the second region of interest, the unsupervised features of the region of interest are obtained through the convolutional autoencoder CAE; based on the unsupervised features, the random forest classifier is used to obtain the classification results of CT images. The invention can obtain the classification of unlabeled CT images.
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technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a CT image classification method, system and device based on semi-supervised deep learning. Background technique [0002] Computed Tomography (CT) has been widely used in health analysis due to its high spatio-temporal resolution and non-invasive features. There is a certain correlation between the morphology, texture and other information of CT images and the category attributes of the images. It is an important application of computer-aided analysis to use the method of image classification to automatically distinguish the category attributes of images, or to calculate the possibility of CT images having certain attributes. [0003] Traditional CT image classification mainly includes two methods: classification methods based on manual features and classification methods based on supervised deep learning. The classification method based on manual features ...
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IPC IPC(8): G06K9/62
CPCG06F18/241
Inventor 田捷王硕刘振宇
Owner INST OF AUTOMATION CHINESE ACAD OF SCI