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

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics.
Faster whole slide image scanning has paved the way for this development, but …

The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …

Mitosis detection is one of the critical factors of cancer prognosis, carrying significant
diagnostic information required for breast cancer grading. It provides vital clues to estimate …

Digital pathology represents a major evolution in modern medicine. Pathological
examinations constitute the standard in medical protocols and the law, and call for specific …

We present an efficient deep learning framework for identifying, segmenting, and classifying
cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained …

Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural
networks to create a model. The application areas of deep learning in radiation oncology …

The complexity of diagnostic (surgical) pathology has increased substantially over the last
decades with respect to histomorphological and molecular profiling. Pathology has steadily …

Introduction Chronic kidney damage is routinely assessed semiquantitatively by scoring the
amount of fibrosis and tubular atrophy in a renal biopsy sample. Although image digitization …