Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …

Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …

Intense interest in applying convolutional neural networks (CNNs) in biomedical image
analysis is wide spread, but its success is impeded by the lack of large annotated datasets in …

Objectives To evaluate and compare the diagnostic performances of a commercialized
artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT …

Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …

Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography
pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and …

Objectives To evaluate the performance of an AI-powered algorithm for the automatic
detection of pulmonary embolism (PE) on chest computed tomography pulmonary …

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image
translation. The development and proliferation of GANs raises an interesting question: can …