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Chestnut: tumors can be found faster and more efficient



The combination of the latest advances in technology has been restored to a medical and patient service. A more efficient and more specific way for identify cancer tumor in pain which analyzes machine images and # 39; Using a computer, a process faster than the traditional one, is awarded by Southern California University (USC).

"The start is to use the & # 39;learning toolsAnd find new information about chronic cancer for the doctor, "said David And, professor at Keck Medicine School and Viterbi Engineering School, USC, and one of the authors at the research.

"We can use this system to establish better treatments, Provide information to patients more quickly and help more people. We are releasing this lock to offer new information to doctors and help them to; cancer treatment, "he said.

The researcher stressed that he has a knowledge of how to do it; diagnosis and cancer treatment. "Cells of cancer containing receptors for estrogen and other hormones answer different from drugs, "he said.

What is the new development based: machine learning

The system "teaching"To a computer to quickly analyze images of cancer chest infection, to identify those who offer estrogen receptors, which are a key factor in medicine options." According to the description of the method, published in the scientific magazine Nature Partner Journals Breast Cancer, it is about "A big step over microscopes and cell biopsies that have been used for more than a hundred years"

"If you are diagnosed with cancer it will be a few weeks before getting a doctor's call telling him to find an identifier, "said Dan Ruderman, professor of medical research at Keck School and co-author of the survey." With machine & we can report on the same day, because there is little delay, less difficulty and results that may be better. This will allow us to identify the correct drug and the dose faster. It's a big step personal treatment", Added Ruderman.

The study focused on the establishment of paramamides to identify the "key identification features" in cellular cells and integrate them into a large network, so that machine technology can be quickly identified. "Learning machine helps us to get more information to patients faster and can change cancer treatment in the developed world, where a rigorous assessment of its chronic cancer mark is scarce, "Rishi Rawat, a graduate from Keck School and the main author of the study, decided.


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