Abstract Purpose of Review Artificial intelligence (AI) technology holds both great promise to
transform mental healthcare and potential pitfalls. This article provides an overview of AI and …

Abstract Machine learning has been developed dramatically and witnessed a lot of
applications in various fields over the past few years. This boom originated in 2009, when a …

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their
uncertainty remains an unresolved challenge, hindering their deployment in consequential …

Pneumonia causes the death of around 700,000 children every year and affects 7% of the
global population. Chest X-rays are primarily used for the diagnosis of this disease …

We propose a random forest classifier for identifying adequacy of liver MR images using
handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the …

Melanoma is the deadliest form of skin cancer. Distinguishing melanoma lesions from non-
melanoma lesions has however been a challenging task. Many Computer Aided Diagnosis …

Medical data is one of the most rewarding and yet most complicated data to analyze. How
can healthcare providers use modern data analytics tools and technologies to analyze and …

Traditional machine learning, mainly supervised learning, follows the assumptions of closed-
world learning, ie, for each testing class, a training class is available. However, such …

Recently, due to the increasing requirements of medical imaging applications and the
professional requirements of annotating medical images, few-shot learning has gained …