Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

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 …

As advances in large language models (LLMs) and multimodal techniques continue to
mature, the development of general-purpose multimodal large language models (MLLMs) …

Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Histopathology plays a central role in clinical medicine and biomedical research. While
artificial intelligence shows promising results on many pathological tasks, generalization …

Recent advances in artificial intelligence show tremendous promise to improve the
accuracy, reproducibility, and availability of medical diagnostics across a number of medical …

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at
the same time a hot topic for fundamental research. Digital pathology is not just the transfer …

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 …

Background Computational pathology has seen rapid growth in recent years, driven by
advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel …

Recent advances in computational algorithms, along with the advent of whole slide imaging
as a platform for embedding artificial intelligence (AI), are transforming pattern recognition …