Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

ABSTRACT: Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and t

SUBMITTER: Ma K 

PROVIDER: S-EPMC10574977 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature