Abstract

Digital pathology is a field that blends various techniques for obtaining, analyzing, sharing, and saving information about pathology. This information often comes from digitized microscope slides. Digital pathology also uses artificial intelligence (AI) to help reduce errors made by humans. This review talks about digital pathology and the new techniques linked to it. Instead of traditional microscopes, digital pathology employs virtual microscopy and whole-slide imaging. It marks a major improvement over old pathology methods, which had several problems. Digital methods use computers and machines to solve these issues. The basic process of digital pathology has three parts: the input stage, the analysis stage, and the output stage, which includes storing the information. This review focuses on two main techniques: object detection and its smaller methods, and the use of AI and its specific approaches like explainable AI (XAI) and deep learning. The paper also discusses various deep learning methods, mainly used to detect different types of cancer. It also acknowledges that not every method is perfect, so we discuss various challenges and limitations of digital pathology techniques that need to be solved before these methods can be widely used.