Semi-Supervised Relational Contrastive Learning

• URL: http://arxiv.org/abs/2304.05047v2
• Date: Tue, 13 Jun 2023 06:34:37 GMT
• Title: Semi-Supervised Relational Contrastive Learning
• Authors: Attiano Purpura-Pontoniere, Demetri Terzopoulos, Adam Wang, Abdullah-Al-Zubaer Imran
• Abstract summary: We present a novel semi-supervised learning model that leverages self-supervised contrastive loss and consistency. We validate against the ISIC 2018 Challenge benchmark skin lesion classification and demonstrate the effectiveness of our method on varying amounts of labeled data.
• Score: 8.5285439285139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the acquisition of valuable insights from readily available unlabeled images. We present Semi-Supervised Relational Contrastive Learning (SRCL), a novel semi-supervised learning model that leverages self-supervised contrastive loss and sample relation consistency for the more meaningful and effective exploitation of unlabeled data. Our experimentation with the SRCL model explores both pre-train/fine-tune and joint learning of the pretext (contrastive learning) and downstream (diagnostic classification) tasks. We validate against the ISIC 2018 Challenge benchmark skin lesion classification dataset and demonstrate the effectiveness of our semi-supervised method on varying amounts of labeled data.

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