Automatically annotated 3D whole-mount pathology image. (Credit: Riken)

Artificial intelligence (AI) technology has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors. Further, the AI identified features relevant to cancer prognosis that were not previously noted by pathologists, leading to a higher accuracy of prostate cancer recurrence compared to pathologist-based diagnosis. Combining the predictions made by the AI with predictions by human pathologists led to an even greater accuracy.

The technology could contribute to personalized medicine by making highly accurate prediction of cancer recurrence possible by acquiring new knowledge from images. It could also contribute to understanding how AI can be used safely in medicine by helping to resolve the issue of AI being seen as a “black box.”

Rather than being “taught” medical knowledge, the AI was asked to learn using unsupervised deep neural networks, known as autoencoders, without being given any medical knowledge. The researchers developed a method for translating the features found by the AI — only numbers initially — into high-resolution images that can be understood by humans. The AI learned using pathology images without diagnostic annotation from 11 million image patches.

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Medical Design Briefs Magazine

This article first appeared in the February, 2020 issue of Medical Design Briefs Magazine.

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