Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the
amount of data from manufacturing systems has been increasing rapidly. With massive …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Abstract Machine learning promises to revolutionize clinical decision making and diagnosis.
In medical diagnosis a doctor aims to explain a patient's symptoms by determining the …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …