Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

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 …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

Histopathology image evaluation is indispensable for cancer diagnoses and subtype
classification. Standard artificial intelligence methods for histopathology image analyses …

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant
treatment. The current gold standard of combined pathological and molecular profiling is …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through
standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can …

Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has
advanced rapidly, influencing cancer care. AI systems are already approved for clinical use …

Despite therapeutic advancements, disease-free survival and overall survival of patients
with locally advanced rectal cancer have not improved in most trials as a result of distant …