Medical image recognition method, model training method, and computer device

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.

A computer device, comprising:

• a memory and a processor configured to,
• obtain a to-be-recognized original medical image set, the to-be-recognized original medical image set comprising at least one to-be-recognized original medical image;
• for each to-be-recognized original medical image of the to-be-recognized original medical image set,
• obtain label information of the respective to-be-recognized original medical image in the to-be-recognized original medical image set, the label information comprising information associated with the respective to-be-recognized original medical image,
• determine whether the label information of the respective to-be-recognized original medical image satisfies a sample extraction condition,
• match the respective to-be-recognized original medical image with a target medical image in a case that the label information of the respective to-be-recognized original medical image is determined to satisfy the sample extraction condition, the target medical image being a preset image template, and
• determine, in a case that the respective to-be-recognized original medical image is successfully matched with the target medical image, that the to-be-recognized original medical image is one of a to-be-recognized medical image set, o the to-be-recognized medical image set comprising at least one to-be-recognized medical image;
• extract a to-be-recognized area corresponding to each to-be-recognized medical image in the to-be-recognized medical image set, the to-be-recognized area being a part of the to-be-recognized medical image;
• and
• determine a recognition result of each to-be-recognized area through a medical image recognition model, the medical image recognition model being obtained through training according to a medical image sample set, the medical image sample set comprising at least one medical image sample, each medical image sample carrying corresponding annotation information, the annotation information being used for representing a type of the medical image sample, the recognition result being used for representing a type of the to-be-recognized medical image.