Capstone Projects.

Utilize Semi-Supervised Consistency Regularized Representational Learning, with strategic target labelling, to significantly reduce the need for fully labelled data in domains (e.g. medical) where labeling data is extremely costly or impractical.

Inspired by the recent advances in self-supervised learning methods, we apply the research work SimCLR from the Google Research Team to retinal OCT image dataset. With the help of self-supervised contrastive learning, our deep learning algorithm is able to learn useful representations of the data in the absence of labeled data, and can be further fine tuned to adapt downstream classification tasks. We evaluate the training sample size threshold at which our framework outperforms a comparable supervised-learning architecture.