Rare diseases (RDs) are complicated health conditions that are difficult to be managed at
several levels. The scarcity of available data chiefly determines an intricate scenario even …

The occurrence of long-tailed distributions and unavailability of high-quality annotated
images is a common phenomenon in medical datasets. The use of conventional Deep …

In clinical scenarios, the two subfields of Artificial Intelligence (AI), ie, Machine Learning (ML)
and Deep Learning (DL) methods have become the de facto standard in several domains of …

Annotated images for diagnosis of rare or novel diseases are likely to remain scarce due to
small affected patient population and limited clinical expertise to annotate images. Deep …

Deep neural networks (DNNs) have been widely applied in the medical image community,
contributing to automatic ophthalmic screening systems for some common diseases …

To benefit the learning of a new task, meta-learning has been proposed to transfer a well-
generalized meta-model learned from various meta-training tasks. Existing meta-learning …

Meta-learning enables algorithms to quickly learn a newly encountered task with just a few
labeled examples by transferring previously learned knowledge. However, the bottleneck of …

Imaging exams, such as chest radiography, will yield a small set of common findings and a
much larger set of uncommon findings. While a trained radiologist can learn the visual …

Generalising deep models to new data from new centres (termed here domains) remains a
challenge. This is largely attributed to shifts in data statistics (domain shifts) between source …

We propose a simple framework—meta-matching—to translate predictive models from large-
scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The …