Patent ID: 11961227
Assignee: PING AN TECHNOLOGY (SHENZHEN) CO., LTD
Field: Computer technology (Electrical engineering)
Classification: CPC G  A | IPC A  G

Claim 1:
2. The method as claimed in claim 1, wherein the deep learning model is obtained by pre-training as follows:
obtaining each medical image sample for training;
for each medical image sample, marking a mark value corresponding to each preset lesion type, and obtaining a mark sequence corresponding to each medical image sample, wherein each element in the mark sequence is the mark value corresponding to each preset lesion type, and wherein in each medical image sample, a mark value corresponding to the preset lesion type that is positive is 1, and a mark value corresponding to the preset lesion type that is negative is 0;
for each marked medical image sample, inputting each medical image sample into the deep learning model for iterative training, and obtaining a sample sequence corresponding to each medical image sample output from the deep learning model, wherein each element in the sample sequence is a second confidence corresponding to each preset lesion type, and wherein the second confidence represents a probability that each medical image sample belongs to a corresponding preset lesion type;
adjusting model parameters of the deep learning model with a calculation result of a preset loss function as an adjustment target until the calculation result of the loss function converges and is less than a preset convergence threshold, wherein the loss function is used for calculating an error between the sample sequence and the mark sequence corresponding to each medical image sample; and
after the calculation result of the loss function converges and is less than the preset convergence threshold, determining that the deep learning model has been trained.