Deep learning offers great opportunities to improve quantitative and qualitative kidney
histology analysis for research and clinical nephropathology diagnostics. Although exciting …

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals.
Cloud computing could open up the field of computer vision to a wider medical audience …

Background Compilation of different morphological lesion signatures is characteristic of
renal pathology. Previous studies have documented the potential value of artificial …

Identification of glomerular lesions and structures is a key point for pathological diagnosis,
treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming …

Recent breakthroughs in the classification of thousands of images into defined categories by
deep learning has led to the application of these algorithms and models to the field of …

Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney
diseases. With the increase in the incidence rate of kidney diseases, the lack of renal …

Kidney diseases result from various causes, which can generally be divided into neoplastic
and non-neoplastic diseases. Deep learning based on medical imaging is an established …

Introduction Artificial intelligence (AI) integration in nephropathology has been growing
rapidly in recent years, facing several challenges including the wide range of histological …

Introduction Evaluating histopathology via machine learning has gained research and
clinical interests, and the performance of supervised learning tasks has been described in …

Once confined to the world of science fiction, advances in information technology,
particularly in computational and storage resources, have enabled use of artificial …