The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

TL;DR

This paper reviews the rapid growth of AI applications in cancer tissue imaging, highlighting core tasks, benefits, challenges, and future directions for improving diagnostics and research in oncology.

Contribution

It provides a comprehensive overview of current AI methods in cancer tissue imaging, identifying key tasks, challenges, and future research directions.

Findings

AI enhances cancer diagnostics and research.

Five core tasks: regression, classification, segmentation, generation, compression.

Challenges include ethical and technical issues.

Abstract

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.