ArXiv Preprint
Computer vision and machine learning are playing an increasingly important
role in computer-assisted diagnosis; however, the application of deep learning
to medical imaging has challenges in data availability and data imbalance, and
it is especially important that models for medical imaging are built to be
trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning
framework for medical image analysis, which adopts a modular design, leverages
self-supervised pre-training, and utilizes a novel surrogate loss function.
Experimental evaluations indicate that models generated from the framework are
both trustworthy and high-performing. It is anticipated that the framework will
support researchers and clinicians in advancing the use of deep learning for
dealing with public health crises including COVID-19.
Kai Ma, Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stéphane Tremblay, Alexander Wong
2022-12-06
