Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Artificial intelligence and machine learning techniques are breaking into biomedical
research and health care, which importantly includes cancer research and oncology, where …

Here we present deep-learning techniques for healthcare, centering our discussion on deep
learning in computer vision, natural language processing, reinforcement learning, and …

Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …

The incidence and mortality rate of Breast Cancer (BC) are global problems for women, with
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …

Convolutional neural network (CNN), a class of artificial neural networks that has become
dominant in various computer vision tasks, is attracting interest across a variety of domains …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …