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 uses artificial intelligence to enable precision medicine and
decision support systems through the analysis of whole slide images. It has the potential to …

Identification of actionable genomic alterations in diagnostic tissue samples provides key
information for personalised cancer treatment. However, current diagnostic tests used to …

Digital pathology has seen a proliferation of deep learning models in recent years, but many
models are not readily reusable. To address this challenge, we developed WSInfer: an open …

In recent years, computer vision techniques have made large advances in image recognition
and been applied to aid radiological diagnosis. Computational pathology aims to develop …

Deep learning methods have emerged as powerful tools for analyzing histopathological
images, but current methods are often specialized for specific domains and software …

Self-supervised learning (SSL) has been successful in building patch embeddings of small
histology images (eg 224 x 224 pixels) but scaling these models to learn slide embeddings …

Deep learning is a powerful tool for assessing pathology data obtained from digitized biopsy
slides. In the context of supervised learning, most methods typically divide a whole slide …

Image‐based biomarker discovery typically requires accurate segmentation of histologic
structures (eg cell nuclei, tubules, and epithelial regions) in digital pathology whole slide …

High computational cost associated with digital pathology image analysis approaches is a
challenge towards their translation in routine pathology clinic. Here, we propose a …