Health IT Analytics June 30, 2020
A new two-step technique to train deep learning algorithms could enhance researchers’ understanding of cancer diagnostics and biology.
A team from the Lawrence J. Ellison Institute for Transformative Medicine of USC have developed a technique to train a deep learning algorithm for improved cancer diagnostics.
In a study published in Scientific Reports, researchers describe using novel tissue fingerprints of tumors paired with correct diagnoses to facilitate deep learning in the classification of breast cancer.
Developing artificial intelligence algorithms for cancer diagnostics is a challenging task, the team noted. These tools need clinically annotated data from tens of thousands of patients to analyze before they can recognize meaningful relationships in the data with consistency. In cancer pathology, an ideal size dataset...
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