Generative adversarial networks (GANs) have gained significant attention in the field of
image synthesis, particularly in computer vision. GANs consist of a generative model and a …

In the realm of medical imaging, the training of machine learning models necessitates a
large and varied training dataset to ensure robustness and interoperability. However …

Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) are
increasingly used for screening genes involved in carcinogenesis due to their capacity for …

Biomolecular piezoelectric materials show great potential in the field of wearable and
implantable biomedical devices. Here, a self‐assemble approach is developed to fabricating …

Histopathology image analysis is paramount importance for accurate diagnosing diseases
and gaining insight into tissue properties. The significant challenge of staining variability …

(1) Background: Considering the increasing workload of pathologists, computer-assisted
methods have the potential to come to their aid. Considering the prognostic role of …

In recent years, artificial intelligence (AI) approaches in computer vision and medical
technology have been combined to create various convenient and accurate tools to assist …

Data augmentation is serving as a critical and fundamental technology to improve model
generalization and performance in a wide spectrum of machine learning tasks. Despite the …

Background Deep learning can extract predictive and prognostic biomarkers from
histopathology whole slide images, but its interpretability remains elusive. Methods We …

Generative Adversarial Networks (GANs) have been at the forefront of image synthesis,
especially in medical fields like histopathology, where they help address challenges such as …