Deep learning artificial intelligence that is widely used to detect objects in images could improve early breast cancer detection, according to a study inScientific Reports.
Deep learning is a subset of machine learning. Its networks are inspired by knowledge of how biological brains, such as those of humans or animals, work. Deep learning networks ‘learn’ from datasets annotated by humans to reach image recognition capabilities similar to those of humans.
Dezso Ribli and colleagues propose an improved Computer Assisted Detection (CAD) system based on state-of-the-art deep learning that can be trained to detect and localize breast lesions. CAD systems have been used over the past two decades to help radiologists detect breast cancer by analyzing mammograms and marking suspicious regions which are then reviewed by a radiologist. However, use of these technologies is expensive ($400 million a year in the US) and their benefits remain controversial.
When tested on a dataset of 115 mammography cases (2 of 4 images per case) with proven cancers, the authors’ improved CAD system classified 90% of malign lesions with very few false positives and without human intervention. Current screening methods, which include assessment by radiologists, correctly detect 77-87% of cancers.
The findings suggest that expensive, traditional CAD methods may be replaced by less expensive deep learning methods that are currently being used to recognize objects - such as dogs and cars - in traditional images, to help radiologists detect more cancers. However, the authors caution that so far, they were only able to test their method on a small dataset of images with proven cancers.
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