Artificial intelligence (AI) solutions that automatically extract information from digital histology
images have shown great promise for improving pathological diagnosis. Prior to routine use …

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 …

When designing a diagnostic model for a clinical application, it is crucial to guarantee the
robustness of the model with respect to a wide range of image corruptions. Herein, an easy …

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows
and research-oriented settings, necessitates human intervention and re-scanning …

The production of pathological slides is a complex task requiring several physical and
chemical procedures that are often done manually. Occasionally, such procedures may end …

Machine learning (ML) models are poised to transform surgical pathology practice. The most
successful use attention mechanisms to examine whole slides, identify which areas of tissue …

Histology whole slide images (WSIs) are extensively used in tumor diagnosis and treatment
planning. However, the presence of artifacts resulting from improper operations during WSI …

Artifacts on digitized Whole Slide Images like blur, tissue fold, and foreign particles have
been demonstrated to degrade the performance of deep convolutional neural networks …

This literature review explores the vital role of both classic and intelligent software testing in
ensuring the quality and safety of medical software. Classic approaches establish a solid …

Histological artifacts pose challenges for both pathologists and Computer-Aided Diagnosis
(CAD) systems, leading to errors in analysis. Current approaches for histological artifact …