Abstract: An image classification method includes: performing image segmentation on an unlabeled sample image to obtain image blocks and performing feature extraction on each image block to obtain an initial image feature set including an initial image feature corresponding to each image block, rearranging and combining initial image features in the initial image feature set to obtain a first image feature set and a second image feature set, first image features in the first image feature set and second image features in the second image feature set corresponding to different rearrangement and combination manners, pre-training an image classification model based on the first image feature set and the second image feature set, the image classification model being configured to classify content in an image, and fine-tuning the pre-trained image classification model based on a labeled sample image.

Abstract: An object recognition method includes extracting, by a first Transformer network, spatial features of a plurality of medical images respectively, the plurality of medical images being images of a same object at different times, and fusing the extracted plurality of spatial features, to obtain a first fusion spatial feature of the object. The method further includes extracting, by a second Transformer network, a spatial-temporal feature of the object based on the first fusion spatial feature. The spatial-temporal feature indicates a change in the spatial features of the plurality of medical images at the different times. The method further includes recognizing a state of the object based on the spatial-temporal feature, to obtain a recognition result of the object.