Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Medical diagnostic image analysis (eg, CT scan or X-Ray) using machine learning is an
efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic …

Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …

Brain tumor diagnosis at an early stage can improve the chances of successful treatment
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …

Scarcity of annotated images hampers the building of automated solution for reliable COVID-
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …

Data-driven approaches have great potential to shape future practices in radiology. The
most straightforward strategy to obtain clinically accurate models is to use large, well …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …