Advances in deep learning for natural images have prompted a surge of interest in applying
similar techniques to medical images. The majority of the initial attempts focused on …

We present a deep convolutional neural network for breast cancer screening exam
classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our …

Some recent studies have described deep convolutional neural networks to diagnose breast
cancer in mammograms with similar or even superior performance to that of human experts …

Background and objective Radiologists often have a hard time classifying mammography
mass lesions which leads to unnecessary breast biopsies to remove suspicions and this …

Mammography is the primary modality for breast cancer screening, attempting to reduce
breast cancer mortality risk with early detection. However, robust screening less hampered …

For AI researchers, access to a large and well-curated dataset is crucial. Working in the field
of breast radiology, our aim was to develop a high-quality platform that can be used for …

The rapid development of deep learning, a family of machine learning techniques, has
spurred much interest in its application to medical imaging problems. Here, we develop a …

Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …

Training a deep convolutional neural network from scratch requires massive amount of data
and significant computational power. However, to collect a large amount of data in medical …

We show two important findings on the use of deep convolutional neural networks (CNN) in
medical image analysis. First, we show that CNN models that are pre-trained using …