Introduction: There has been a rapid development of deep learning (DL) models for medical
imaging. However, DL requires a large labeled dataset for training the models. Getting large …

Visual representation extraction is a fundamental problem in the field of computational
histopathology. Considering the powerful representation capacity of deep learning and the …

Fibrosis is an inevitable stage in the development of chronic liver disease and has an
irreplaceable role in characterizing the degree of progression of chronic liver disease …

Digital Pathology is an area prone to high variation due to multiple factors which can
strongly affect diagnostic quality and visual appearance of the Whole-Slide-Images (WSIs) …

The application of supervised deep learning methods in digital pathology is limited due to
their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to …

Convolutional neural network (CNN)-based image analysis applications in digital pathology
(eg, tissue segmentation) require a large amount of annotated data and are mostly trained …

Background In digital pathology, many image analysis tasks are challenged by the need for
large and time-consuming manual data annotations to cope with various sources of …

Virtual stain transfer is a promising area of research in Computational Pathology, which has
a great potential to alleviate important limitations when applying deep-learning-based …

Domain shift in digital histopathology can occur when different stains or scanners are used,
during stain translation, etc. A deep neural network trained on source data may not …

The field of digital pathology emerged with the introduction of whole slide imaging scanners
and lead to the development of new tools for analyzing histopathological slides. The …