In a recent study published in the journal Nature Medicine , a large team of researchers in the United States discussed the use of the foundational model Virchow for computational analysis of pathological reports and demonstrated its use in histopathological analysis to predict biomarkers and identify cells across seven rare and nine common forms of cancer. The training dataset, training algorithm and application of Virchow, a foundation model for computational pathology. a , The training data can be described in terms of patients, cases, specimens, blocks or slides, as shown.
b – d , The slide distribution as a function of cancer status ( b ), surgery ( c ) and tissue type ( d ). e , The dataflow during training requires processing the slide into tiles, which are then cropped into global and local views. f , Schematic of applications of the foundation model using an aggregator model to predict attributes at the slide level.
GI, gastrointestinal. Study: A foundation model for clinical-grade computational pathology and rare cancers detection Background The diagnosis of cancers has traditionally depended on the examination of histopathological preparations of hematoxylin and eosin slides using light microscopes. The advances in digital technology and computational pathology have replaced this with whole-slide images that can be examined computationally, making this form of diagnosis a part of routine clinical practice.
The use of artificial intelligence (AI) in the diagnosis .