The advent of whole slide imaging has brought advanced computer-aided diagnosis via
medical imaging and artificial intelligence technologies in digital pathology. The …

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital
revolution. Simultaneously, with the development of image analysis algorithms based on …

Color medical images introduce an additional confounding factor compared to conventional
grayscale medical images: color variability. This variability can lead to inconsistent …

In digital pathology, different staining procedures and scanners cause substantial color
variations in whole-slide images (WSIs), especially across different laboratories. These color …

Color inconsistency is an inevitable challenge in computational pathology, which harms the
pathological image analysis methods, especially the learning-based models. A series of …

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) …

In digital pathology, the final appearance of digitized images is affected by several factors,
resulting in stain color and intensity variation. Stain normalization is an innovative solution to …

Data-driven remote vital sign estimation provides an efficient alternative to on-site clinical
monitoring, however, its performance can be biased due to the imbalanced training sets. In …

With the digitization of histopathology, machine learning algorithms have been developed to
help pathologists. Color variation in histopathology images degrades the performance of …

Microscopic analyses of tissue samples are crucial for confirming the diagnosis of breast
cancer. The digitization of these samples has led to the development of computational …