Importance Application of deep learning algorithms to whole-slide pathology images can
potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of …

Pathologists face a substantial increase in workload and complexity of histopathologic
cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols …

Background Transplant nephropathology is a highly specialized field of pathology comprising
both the evaluation of organ donor biopsy for organ allocation and post-transplant graft …

Automated detection of cancer metastases in lymph nodes has the potential to improve the
assessment of prognosis for patients. To enable fair comparison between the algorithms for …

Background The presence of lymph node metastases is one of the most important factors in
breast cancer prognosis. The most common way to assess regional lymph node status is the …

Background The development of deep neural networks is facilitating more advanced digital
analysis of histopathologic images. We trained a convolutional neural network for multiclass …

Currently, histopathological tissue examination by a pathologist represents the gold
standard for breast lesion diagnostics. Automated classification of histopathological whole-slide …

Deep learning has pushed the scope of digital pathology beyond simple digitization and
telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and …

The performance of deep learning applications in digital histopathology can deteriorate
significantly due to staining variations across centers. We employ cycle-consistent generative …

In kidney transplant biopsies, both inflammation and chronic changes are important features
that predict long-term graft survival. Quantitative scoring of these features is important for …