Background: Deep learning (DL) is a representation learning approach ideally suited for
image analysis challenges in digital pathology (DP). The variety of image analysis tasks in …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

With the rise in whole slide scanner technology, large numbers of tissue slides are being
scanned and represented and archived digitally. While digital pathology has substantial …

In recent years, computer vision techniques have made large advances in image recognition
and been applied to aid radiological diagnosis. Computational pathology aims to develop …

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is
mastering computer vision tasks, its application to digital pathology is natural, with the …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

Recent advances in artificial intelligence show tremendous promise to improve the
accuracy, reproducibility, and availability of medical diagnostics across a number of medical …

Recent advances in computational algorithms, along with the advent of whole slide imaging
as a platform for embedding artificial intelligence (AI), are transforming pattern recognition …

There has been an exponential growth in the application of AI in health and in pathology.
This is resulting in the innovation of deep learning technologies that are specifically aimed at …