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

Thyroid nodule is one of the common life‐threatening diseases, and it had an increasing
trend over the last years. Ultrasound imaging is a commonly used diagnostic method for …

Visual microscopic study of diseased tissue by pathologists has been the cornerstone for
cancer diagnosis and prognostication for more than a century. Recently, deep learning …

Despite the growing availability of high-quality public datasets, the lack of training samples
is still one of the main challenges of deep-learning for skin lesion analysis. Generative …

Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …

Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …

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 …

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and
has become a cornerstone of modern precision oncology. Histopathological evaluation is …

Artificial intelligence methods including deep neural networks (DNN) can provide rapid
molecular classification of tumors from routine histology with accuracy that matches or …

Histopathological images of tumors contain abundant information about how tumors grow
and how they interact with their micro-environment. Better understanding of tissue …