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

Personalized treatment strategies for cancer frequently rely on the detection of genetic
alterations which are determined by molecular biology assays. Historically, these processes …

Current diagnosis of glioma types requires combining both histological features and
molecular characteristics, which is an expensive and time-consuming procedure …

Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …

The early diagnosis of cancer is crucial to provide prompt and adequate management of the
diseases. Imaging tests, in particular magnetic resonance imaging (MRI), are the first …

Diagnosis of primary brain tumors relies heavily on histopathology. Although various
computational pathology methods have been developed for automated diagnosis of primary …

Artificial intelligence (AI) can extract complex information from visual data. Histopathology
images of gastrointestinal (GI) and liver cancer contain a very high amount of information …

Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …

Gliomas are primary brain tumors caused by glial cells. These cancers' classification and
grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially …

Digital pathology is gaining prominence among the researchers with developments in
advanced imaging modalities and new technologies. Generative adversarial networks …