In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Hepatocellular carcinoma (HCC) is one of the leading cancers worldwide. Classically, HCC
develops in genetically susceptible individuals who are exposed to risk factors, especially in …

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 …

Digital pathology poses unique computational challenges, as a standard gigapixel slide may
comprise tens of thousands of image tiles,–. Prior models have often resorted to …

Computational pathology, has witnessed considerable progress in the development of both
task-specific predictive models and task-agnostic self-supervised vision encoders …

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 …

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

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