Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets
Andrea Espis; Chiara Marzi; Stefano Diciotti · 2025 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Self-supervised learning (SSL) in computer vision has shown its potential to reduce reliance on labeled data. However, most studies focused on balanced, large, broad-domain datasets like ImageNet, whereas, in real-world medical applications, dataset size is typically limited. This study compares the performance of SSL versus supervised learning (SL) on small, imbalanced medical imaging datasets. We experimented with four binary classification tasks: age prediction and diagnosis of Alzheimer's disease from brain magnetic resonance imaging scans, pneumonia from chest radiograms, and retinal dise
Abstract by Andrea Espis; Chiara Marzi; Stefano Diciotti, Scientific Reports (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-025-99000-0