Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification.

ABSTRACT: In this study, we propose a novel approach to enhancing transfer learning by optimizing data selection through deep learning techniques and corresponding innovative learning strategies. This method is particularly beneficial when the available dataset has reached its limit and cannot be further expanded. Our approach focuses on maximizing the use of existing data to improve learning outcomes which offers an effective solution for data-limited applications in medical imaging classification. The proposed method consists of two stages. In the first stage, an original network performs the initial classification. When the original network exhibits low confidence in its predictions, ambiguous classifications are passed to a secondary decision-making step involving a newly trained network, referr

SUBMITTER: Lee H 

PROVIDER: S-EPMC12062297 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature