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An attention-enhanced brain tumor-assisted intelligent detection and recognition method

A technology of intelligent detection and identification method, which is applied in the field of medical imaging and image processing, can solve the problems involving less, and achieve the effect of improving the diagnostic ability

Active Publication Date: 2022-07-26
BEIHANG UNIV
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Problems solved by technology

[0006] The current application of deep learning on brain MRI images, especially other related patents, is focused on the field of brain tumor segmentation, and less involved in areas directly related to diagnosis and treatment, such as classification and grading of brain tumors, which are clinical It is more concerned about and it is difficult for the human eye to do non-invasive image inspection

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  • An attention-enhanced brain tumor-assisted intelligent detection and recognition method
  • An attention-enhanced brain tumor-assisted intelligent detection and recognition method
  • An attention-enhanced brain tumor-assisted intelligent detection and recognition method

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Embodiment Construction

[0032] See attached manual Figure 1-2 The present invention proposes a method that combines medical images with deep learning and computer vision methods, and uses an analysis method of computer vision to analyze and process three-dimensional brain MRI images, and to segment and process glioma lesions on the brain MRI images. Image-based classification and diagnosis tasks. Aiming at the problems of small amount of data in medical image datasets, serious category imbalance, and existing methods focus on segmentation of lesion areas while ignoring the task of classification and diagnosis, an improved 3D U-Net convolutional neural network is proposed to increase classification and diagnosis. Branch, through the multi-task joint training method, the segmentation and classification results are obtained at the same time. figure 1It is the algorithm design process proposed by the present invention. First, the MRI image and its corresponding manually labeled segmentation results and...

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Abstract

The invention realizes a set of attention-enhanced brain tumor auxiliary intelligent detection and identification method, the technical scheme is improved on the basis of the U-Net model, and proposes to use the training of the segmentation task as the attention enhancement mechanism of the classification task. The focus on tasks, lesion areas and edge information improves the accuracy of classification tasks, and through multi-task loss measurement and training methods, the segmentation task and the classification task are optimized at the same time to achieve the expected results of the segmentation task and the classification task. , to achieve design goals and application goals.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an attention-enhanced brain tumor-assisted intelligent detection and identification method in the field of medical imaging and computer-aided diagnosis. Background technique [0002] Tumors that grow in the brain are collectively referred to as brain tumors, which refer to tumors of the nervous system that occur in the cranial cavity, including tumors originating from neuroepithelial, peripheral nerve, meninges, and germ cells, lymphoid and hematopoietic tissue tumors, and craniopharynx in the sella region. Angiomas and granulosa cell tumors, and metastatic tumors. Tumors that arise from the brain parenchyma are called primary intracranial tumors, and those that metastasize to the brain from malignant tumors of other organs and tissues of the body are called secondary intracranial tumors. Intracranial tumors can occur at any age, with 20-50 years being the most common. In recent...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/084G06T2207/10088G06T2207/30016G06T2207/30096G06N3/045
Inventor 李建欣张帅于金泽周号益邰振赢
Owner BEIHANG UNIV