How Deep Learning Helps Analyze Cancer Tissue

Improvements in computer vision and deep learning may be boosting efficiency in the field of radiology

The explosion of data storage capability and concomitant drop in cost makes all this possible. The multiple layers of abstraction compiled by deep learning is nudging the computerized interpretation of medical imagery forward. The goal is to recognize patterns better and faster than humans can. Today, radiologists still out-perform machines, but the computers are gaining ground. Improvements in cancer recognition sensitivity are encouraging, and one analysis indicated that a deep learning system yielded a lower false-negative rate than humans. Other researchers reported better detail and far higher speeds than traditional procedures.

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