AI prediction method, apparatus and equipment for cerebral infarction, and storage medium
A prediction method and cerebral infarction technology, applied in the field of information processing, can solve problems such as stroke and inability to provide assistance to patients
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Embodiment 1
[0052] See figure 1 , figure 1 It is a schematic diagram of the AI prediction device for cerebral infarction provided by Embodiment 1 of the present invention, which is used to implement the AI prediction method for cerebral infarction provided by the embodiment of the present invention, such as figure 1 As shown, the AI prediction device for cerebral infarction includes: at least one processor 11, such as CPU, at least one network interface 14 or other user interface 13, memory 15, at least one communication bus 12, and the communication bus 12 is used to realize the communication between connections. Wherein, the user interface 13 may optionally include a USB interface, other standard interfaces, and a wired interface. The network interface 14 may optionally include a Wi-Fi interface and other wireless interfaces. The memory 15 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. Th...
Embodiment 2
[0062] figure 2 It is a schematic flowchart of an AI prediction method for cerebral infarction provided by Embodiment 2 of the present invention.
[0063] An AI prediction method for cerebral infarction, comprising the following steps:
[0064] S11. Obtain a training sample; wherein, the training sample includes an original CT image and a corresponding cerebral infarction label;
[0065] S12. Obtain the trained convolutional neural network based on the encoding and decoding architecture; wherein, the trained convolutional neural network based on the encoding and decoding architecture performs convolution neural network on the training samples and the initial convolutional neural network. Network training, using dice coefficients to evaluate prediction results during training, and performing backpropagation on the initial convolutional network according to the evaluation results.
[0066] S13. Preprocessing the CT image to be predicted to obtain a test sample;
[0067] S14....
Embodiment 3
[0101] see Figure 4 , a schematic structural diagram of the AI prediction device for cerebral infarction provided by the third embodiment of the present invention;
[0102] An AI prediction device for cerebral infarction, comprising:
[0103] A training sample acquisition module 31, configured to acquire a training sample; wherein, the training sample includes an original CT image and a corresponding cerebral infarction label;
[0104] The network acquisition module 32 is used to obtain the trained convolutional neural network based on the encoding and decoding architecture; wherein, the trained convolutional neural network based on the encoding and decoding architecture passes through the training sample and the initial convolutional neural network. The network performs convolutional neural network training, uses dice coefficients to evaluate prediction results during training, and performs backpropagation on the initial convolutional network according to the evaluation r...
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