SEMI-SUPERVISED LEARNING-BASED IMAGE CLASSIFICATION METHOD AND APPARATUS, AND COMPUTER DEVICE

摘要

The present application relates to the field of artificial intelligence. Discloses are a semi-supervised learning-based image classification method and apparatus, a computer device and a storage medium. The method comprises: obtaining an OCT image to be classified; processing said OCT image by using a feature vector generator in a preset OCT image classification model to obtain a first feature vector X generated by a first encoder; decoding the first feature vector X by using a first decoder to obtain a decoded image; generating a second feature vector Y by using a second encoder; calculating a similarity value between the first feature vector X and the second feature vector Y, and determining whether the similarity value is greater than a preset similarity threshold; and if the similarity value is greater than the preset similarity threshold, classifying said OCT image as a negative image. Therefore, OCT image classification is completed without positive data, and the defect of difficulty in collecting positive data is overcome.