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 …

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 …

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

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 …

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 …

Artificial intelligence (AI) systems are increasingly used in health and personalized care.
However, the adoption of data-driven approaches in many clinical settings has been …

Advancements in digital pathology and computing resources have made a significant impact
in the field of computational pathology for breast cancer diagnosis and treatment. However …

Tissue histopathology, based on hematoxylin and eosin (H&E) staining of thin tissue slices,
is the gold standard for the evaluation of the immune reaction to the implant of a biomaterial …