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

Background Artificial intelligence (AI) is influencing our society on many levels and has
broad implications for the future practice of hematology and oncology. However, for many …

Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

Although generative adversarial networks (GANs) can produce large datasets, their limited
diversity and fidelity have been recently addressed by denoising diffusion probabilistic …

Background Grading of cancer histopathology slides requires more pathologists and expert
clinicians as well as it is time consuming to look manually into whole-slide images. Hence …

Background Histopathological assessment of transplant biopsies is currently the standard
method to diagnose allograft rejection and can help guide patient management, but it is one …

Deep learning is a powerful tool in computational pathology: it can be used for tumor
detection and for predicting genetic alterations based on histopathology images alone …

Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …

In the past few decades, according to the rapid development of information technology,
artificial intelligence (AI) has also made significant progress in the medical field. Colorectal …

The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation
and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE …