Computational Pathology: A Survey Review and The Way Forward
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Computational Pathology (CoPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CoPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology facilitating transformational changes in the diagnosis and treatment of cancer diseases. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CoPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CoPath. In this article we provide a comprehensive review of more than 700 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CoPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CoPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CoPath.
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