Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Digital pathology provides a possibility for computational analysis of histological slides and
automatization of routine pathological tasks. Histological slides are very heterogeneous …

Abstract Machine learning model deployment in clinical practice demands real-time risk
assessment to identify situations in which the model is uncertain. Once deployed, models …

Bone marrow (BM) examination is an essential step in both diagnosing and managing
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …

Background Clinicians and pathologists traditionally use patient data in addition to clinical
examination to support their diagnoses. Objectives We investigated whether a combination …

Digital whole slides images contain an enormous amount of information providing a strong
motivation for the development of automated image analysis tools. Particularly deep neural …

Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human‐like
intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal …

Deep learning (DL) has become one of the major approaches in computational
dermatopathology, evidenced by a significant increase in this topic in the current literature …

The volume and complexity of scientific and clinical data in oncology have grown markedly
over recent years, including but not limited to the realms of electronic health data …