Abstract: A method for contrastive learning by selecting dropout ratios and locations based on reinforcement learning includes receiving training data having a positive sample corresponding to a target and negative samples not corresponding to the target. A dropout policy for a neural network is produced based on the training data, where the dropout policy identifies at least one connection between neurons in the neural network to dropout. The training data is encoded, based on the dropout policy, to form embeddings, where the embeddings include multiple positive sample embeddings corresponding to the positive sample and multiple negative sample embedding corresponding to the negative samples.

Abstract: The present disclosure provides an image clipping method. The method includes obtaining original volume data, labeling a region of interest in the volume data, clipping the original volume data, and generating an image based on the clipped volume data. The labeled region of interest is defined as a non-clipping object such that only volume data other than the region of interest is clipped.