The invention discloses an image sample
upsampling method based on
convolution self-coding, and the method comprises the steps of carrying out the
cutting of each three-dimensional magnetic resonanceimaging sample, obtaining two-dimensional images of a region where a tumor is located through
cutting, and carrying out the scale normalization of all the two-dimensional images; building a
network structure in a form of
cascade connection of an
encoder and a decoder, and serving as a model; training the model by setting a learning rate and a
loss function; carrying out optimization
processing onthe trained model by adopting an adaptive moment
estimation optimizer; inputting any random
positive sample into the trained network to obtain low-dimensional features extracted by the
encoder, calculating
Euclidean distance center points of eight groups of features, and randomly selecting one group of features from the eight groups of features to obtain new features; and inputting the new features into a decoder for image reconstruction, and outputting a
positive sample image. According to the method, the
feature extraction is carried out through the
encoder, sample enhancement is carried outon samples at the feature level, image reconstruction is carried out through the decoder,
upsampling of a few types of samples is obtained, and the method can be used for balance preprocessing of classification problems.