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

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

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

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

In this white paper, experts from the Digital Pathology Association (DPA) define terminology
and concepts in the emerging field of computational pathology, with a focus on its …

Various deep learning methods have been proposed to segment breast lesions from
ultrasound images. However, similar intensity distributions, variable tumor morphologies …

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 …

Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer
can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important …

Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …