Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

RNA molecules adopt three-dimensional structures that are critical to their function and of
interest in drug discovery. Few RNA structures are known, however, and predicting them …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Artificial intelligence (AI) now enables automated interpretation of medical images. However,
AI's potential use for interventional image analysis remains largely untapped. This is …

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as
a response to the need for increased transparency and trust in AI. This is particularly …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …