Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges

Understand

Deep learning techniques are increasingly being adopted in diagnostic medical imaging.

• However, the limited availability of high-quality, large-scale medical datasets presents a significant challenge, often necessitating the use of transfer learning approaches.
• This study investigates self-supervised learning methods for pre-training capsule networks in polyp diagnostics for colon cancer.
• We used the PICCOLO dataset, comprising 3,433 samples, which exemplifies typical challenges in medical datasets: small size, class imbalance, and distribution shifts between data splits.