Patent ID: 11961226
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC A  G

Claim 5:
6. A model training method, applied to a computer device, the method comprising:
obtaining a to-be-trained original medical image set, the to-be-trained original medical image set comprising at least one to-be-trained original medical image;
for each to-be-trained original medical image of the to-be-trained original medical image set
obtaining label information of the respective to-be-trained original medical image in the to-be-trained original medical image set, the label information comprising information associated with the respective to-be-trained original medical image,
determining whether the label information of the respective to-be-trained original medical image satisfies a sample extraction condition,
matching the respective to-be-trained original medical image with a target medical image in a case that the label information of the respective to-be-trained original medical image is determined to satisfy the sample extraction condition, the target medical image being a preset image template, and
determining, in a case that the respective to-be-trained original medical image is successfully matched with the target medical image, that the to-be-trained original medical image is one of a to-be-trained medical image set, the to-be-trained medical image sample set comprising at least one to-be-trained medical image, each to-be-trained medical image carrying corresponding annotation information, the annotation information being used for representing a type of the to-be-trained medical image;

extracting, with circuitry of the computer device, a to-be-trained area corresponding to each to-be-trained medical image in the to-be-trained medical image sample set, the to-be-trained area being a part of the to-be-trained medical image; and
obtaining a medical image recognition model through training according to the to-be-trained area corresponding to each to-be-trained medical image sample and the corresponding annotation information carried by each to-be-trained medical image sample.