Carotid artery vulnerability grading method and system based on multi-modal radiomics
A radiomics and grading method technology, applied in the field of medical image recognition, can solve problems such as easy omission of hypoechoic plaques, poor ability to identify plaque components, and large influence of subjective judgments by testers
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Embodiment 1
[0035] like figure 1 As shown, this embodiment provides a carotid artery vulnerability grading method based on multimodal radiomics. In this embodiment, the method is applied to the server as an example. It can be understood that this method can also be applied to The terminal can also be applied to include the terminal, the server and the system, and is realized through the interaction between the terminal and the server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud service, cloud database, cloud computing, cloud function, cloud storage, network server, cloud communication, intermediate Cloud servers for basic cloud computing services such as software services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, e...
Embodiment approach
[0051] As one or more implementations, the feature fusion refers to first connecting two feature layers and then training a weight network through a downsampling layer and a softmax layer according to the guidance of a common label to obtain a new weight feature layer.
[0052] As one or more embodiments, the NMR attention classification network is to learn the weight value according to the label guidance after parallel connection of the high-level features extracted from the MRI image and the fused features, and then train to obtain an output result.
[0053] As one or more embodiments, the ultrasonic image attention classification network is to learn the weight value according to the label guidance after parallel connection of the high-level features extracted from the ultrasonic image and the fused features, and then train to obtain an output result.
[0054] As one or more implementations, the result output module 4 outputs the grading results of ultrasound images and MRI i...
Embodiment 2
[0056] This embodiment provides a carotid artery vulnerability grading system based on multimodal radiomics.
[0057] A multimodal radiomics-based carotid arterial vulnerability grading system including:
[0058] a data acquisition module configured to: acquire at least one of a carotid artery ultrasound image and a nuclear magnetic resonance image;
[0059] an output module, configured to: obtain a carotid artery plaque vulnerability grade by adopting a carotid artery plaque grading model based on at least one of the carotid artery ultrasound image and the nuclear magnetic resonance image;
[0060] A model building module configured as: the carotid artery plaque grading model includes: a multi-scale feature fusion network and an attention classification network; wherein the multi-scale feature fusion network is used for carotid artery ultrasound image samples based on carotid artery ultrasound image samples and carotid artery MRI image samples, fuse the extracted carotid art...
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