Learning from deep learning: a case study of feature discovery and validation in pathology
Published in Artificial Intelligence, Tools.
When a patient is diagnosed with cancer, one of the most important steps is examination of the tumor under a microscope by pathologists to determine the cancer stage and to characterize the tumor. This information is central to understanding clinical prognosis (i.e., likely patient outcomes) and for determining the most appropriate treatment, such as undergoing surgery alone versus surgery plus chemotherapy. Developing machine learning (ML) tools in pathology to assist with the microscopic review represents a compelling research area with many potential applications.
Previous studies have shown that ML can accurately identify and classify tumors in pathology images and can even predict patient prognosis using known pathology features, such as the degree to which gland appearances deviate from normal. While these efforts focus on using ML to detect or quantify known features, alternative approaches offer the potential to identify novel features. The discovery of new features could in turn further improve cancer prognostication and treatment decisions for patients by extracting information that isn’t yet considered in current workflows.
https://ai.googleblog.com/2023/03/learning-from-deep-learning-case-study.html