Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Transcriptome level expression data connected to the spatial organization of the cells and
molecules would allow a comprehensive understanding of how gene expression is …

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve
the integration of artificial intelligence (AI)-based systems into routine practice to support the …

Diagnostic workup of cancer patients is highly reliant on the science of pathology using
cytopathology, histopathology, and other ancillary techniques like immunohistochemistry …

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to
immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using …

Abstract Background and Objective Histologic assessment of the immune infiltrate in H&E
slides is vital in diagnosing and managing inflammatory bowel diseases, but these …

A substantial body of research has been published in artificial intelligence due to the rising
incidence of skin cancer, the scarcity of specialized healthcare professionals, and rapid …

Deep learning involves an artificial intelligence (AI) approach and has been shown to
provide superior performance for automating image recognition tasks, as well as exceeding …

Aims Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for
clinical decision‐making, especially in the early stage. However, the inter‐and intraobserver …

Early detection is crucial for increasing the chance of survival in Ovarian Cancer (OC), as it
is a very challenging illness to treat that often leads to death. Ultrasound (UT), Magnetic …