Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

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 …

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) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies.
However, results have been limited to individual studies, lacking validation in multinational …

Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …

Abstract The 2022 World Health Organization (WHO) classification of the urinary and male
genital tumors was recently published by the International Agency for Research on Cancer …

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive
nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging …