Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …

A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …

Extracting, harvesting, and building large-scale annotated radiological image datasets is a
greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations …

Objectives: To investigate the current clinical applications and diagnostic performance of
artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). Methods: Studies …

Artificial intelligence in medicine has made dramatic progress in recent years. However,
much of this progress is seemingly scattered, lacking a cohesive structure for the discerning …

The technique of force and haptic reappearance is an effective method to solve the shortage
of haptic presence and improve the medical robots' practicability. Soft tissue models, the …

Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

In this article, the authors propose an ethical framework for using and sharing clinical data
for the development of artificial intelligence (AI) applications. The philosophical premise is …