METHOD FOR TRAINING IMAGE RECOGNITION MODEL, AND METHOD AND APPARATUS FOR IMAGE RECOGNITION

Disclosed in the present application is a method for training an image recognition model, comprising: acquiring image sets to be trained; on the basis of the image sets to be trained, and by means of an image recognition model to be trained, acquiring a first prediction probability, a second prediction probability, a third prediction probability, and a fourth prediction probability; determining a target loss function according to the first prediction probability, the second prediction probability, the third prediction probability, and the fourth prediction probability; and, on the basis of the target loss function, training said image recognition model so as to obtain an image recognition model. Further disclosed in the present application are a method and apparatus for image recognition. The present application employs labeled medical images for different tasks and unlabeled medical images to jointly train a model, effectively using labeled images and unlabeled images, which reduces the need for image labeling, and also increases the amount of data for training. Therefore, the prediction effect of the model may be improved while saving labeling resources.