Computational histopathology involves CAD for microscopic analysis of stained
histopathological slides to study presence, localization or grading of disease. An important …

Performance of designed CAD algorithms for histopathology image analysis is affected by
the amount of variations in the samples such as color and intensity of stained images. Stain …

Deep learning has shown its effectiveness in histopathology image analysis, such as
pathology detection and classification. However, stain colour variation in Hematoxylin and …

It is generally recognized that color information is central to the automatic and visual
analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects …

Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …

Due to differences in tissue preparations, staining protocols and scanner models, stain
colors of digitized histological images are excessively diverse. Color normalization is almost …

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

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through
examining the morphology of the tissue slices and the spatial arrangement of the cells. If the …

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

Digitized Histological diagnosis is in increasing demand. However, color variations due to
various factors are imposing obstacles to the diagnosis process. The problem of stain color …