Medical imaging refers to several different technologies that are used to view the human
body to diagnose, monitor, or treat medical conditions. It requires significant expertise to …

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence
and are designed to “learn” from annotated data. Deep-learning models require large …

Biomedical image analysis algorithm validation depends on high-quality annotation of
reference datasets, for which labelling instructions are key. Despite their importance, their …

The splendid success of convolutional neural networks (CNNs) in computer vision is largely
attributable to the availability of massive annotated datasets, such as ImageNet and Places …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

This book was partially motivated by the recent rapid progress on deep convolutional and
recurrent neural network models and the abundance of important applications in computer …

Medical image annotation is a major hurdle for developing precise and robust machine-
learning models. Annotation is expensive, time-consuming, and often requires expert …

Purpose To develop and evaluate domain-specific and pretrained bidirectional encoder
representations from transformers (BERT) models in a transfer learning task on varying …

The lack of annotated datasets is a major bottleneck for training new task-specific
supervised machine learning models, considering that manual annotation is extremely …

The interest in artificial intelligence (AI) applications in medical imaging has grown rapidly in
the past few years, largely driven by the success of deep learning. Methods ranging from …