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

Histological staining is the gold standard for tissue examination in clinical pathology and life-
science research, which visualizes the tissue and cellular structures using chromatic dyes or …

Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …

Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …

The recent boom in microfluidics and combinatorial indexing strategies, combined with low
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …

Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

Big medical data mainly include electronic health record data, medical image data, gene
information data, etc. Among them, medical image data account for the vast majority of …