Background: The use of artificial intelligence (AI) in health care has grown exponentially with
the promise of facilitating biomedical research and enhancing diagnosis, treatment …

This review synthesises past research into how machine and deep learning can improve the
cyto-and histopathology processing pipelines for thyroid cancer diagnosis. The current gold …

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing
thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy …

Context Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule
diagnostics has grown exponentially over the past decade. The greatest challenge is in …

Thyroid cancer is a life-threatening condition that arises from the cells of the thyroid gland
located in the neck's frontal region just below the adam's apple. While it is not as prevalent …

Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …

The ability to profile spatial proteomics at the single cell level enables the study of cell types,
their spatial distribution, and interactions in several tissues and conditions. Current methods …

Supervised deep learning approaches for automated diagnosis support require datasets
annotated by experts. Intra-annotator variability of a single annotator and inter-annotator …

Deep learning models achieve high-quality results in image processing. However, to
robustly optimize parameters of deep neural networks, large annotated datasets are …

A world of healthcare possibilities has been opened with the development of the Internet of
Medical Things and related machine learning, deep learning, and artificial intelligence …