Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two learners have large difference, which explai...

breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...

Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learning. In this paper, we propose a novel self-supervised method that leverages multiple imaging modalities. We introduce the multimodal puzzle task, which facilitates representation from image The learned modality-agnost...