Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of
digital pathology (DP) and an increase in computational power have led to the development …

The success of training computer-vision models heavily relies on the support of large-scale,
real-world images with annotations. Yet such an annotation-ready dataset is difficult to …

Introduction Artificial intelligence (AI) is currently being used to augment histopathological
diagnostics in pathology. This systematic review aims to evaluate the evolution of these AI …

Context.—Recent developments in machine learning have stimulated intense interest in
software that may augment or replace human experts. Machine learning may impact …

Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …

Artificial intelligence has been widely used for digital pathology diagnosis. However, the AI
performance highly relies on the high-quality annotated datasets, pathological images need …

Despite the revolutionary developments potentiated by modern machine learning, several
challenges remain, including data quality control and curation, image annotation and …

As digital pathology replaces conventional glass slide microscopy as a means of reporting
cellular pathology samples, the annotation of digital pathology whole slide images is rapidly …

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize
the true potential of deep learning in histopathology applications. Although slide digitization …

Digital pathology is an image-based environment for the acquisition, management, sharing,
and interpretation of pathology information supported by techniques for data extraction and …