An AI tool boosts breast cancer detection accuracy

Original article was published by Data Overflow on Artificial Intelligence on Medium

An AI tool boosts breast cancer detection accuracy

Breast cancer is the most-common invasive cancer among women. It affects 14% of women worldwide and every year, over half a million women die of it. That makes breast cancer one of the largest medical problems faced today.

Taking treatment decisions and curing this disease requires, at first place, diagnosing it.

However, due to time constraints and the existence of very small metastases on individual slides, human pathologists sometimes fail to detect cancerous cells: they miss about 20% of breast cancers in mammograms, even in countries where screening mammograms are reviewed by two radiologists.

In the last years, the introduction of deep learning and convolutional neural networks (CNNs) in medical image analysis has prevaded the field of automated breast cancer detection in digital mammography.

Researchers have created an AI diagnostic tool that helps doctors detect breast cancer.

The man-made neural network is trained with more than a million mammography images, and the program is getting “smarter” as it reviews more and more data.

The AI tool was taught to analyze even the small patches, spot changes that are invisible to the human, and then make a map of the areas that are most at risk.

In addition to its success in the detection of abnormality as well as an average radiologist, this AI invention has won two main challenges concerning the efficiency and consistence of the results: Reducing false negatives by 9% and false positives by 5.7%.

At this level, you might be asking yourself whether or not this program will replace doctors when it comes to detecting breast cancer. The answer is definitely a NO!

In fact, the knowledge we’re getting when the program points out cancer to us, is the knowledge of many pathologists who contributed to training the machine on a model. It’s mainly human knowledge.

All we said above, highlights the fact that we must combine the strengths of human radiologists with the results of these performing systems in order to provide the most accurate diagnosis for the patient and thus allow clinicians to have significantly more time to deal with curing him.

“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI,” says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone and an affiliated faculty member at the NYU Center for Data Science. “The ultimate goal of our work is to augment, not replace, human radiologists.”


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