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Google's latest achievements: the use of neural networks to get rid of people with diabetes blindness "curse"

via:博客园     time:2017/5/19 12:00:53     readed:834

According to Wikipedia, as of 2014, more than 420 million people worldwide have diabetes, this figure has been reduced in recent years, but the situation is still not optimistic. As one of the complications of diabetes, diabetic retinopathy (Diabetic Retinopathy) is eroding long-term diabetes patients. Medical people found that for the general patients, more than 10 illness will begin to appear lesions, leading to blindness. It sounds very far after 10 years, but the situation is actually more urgent than imagined, because those who are poor blood glucose control, or insulin-dependent diabetes patients, they are likely to have early eye lesions, the risk of blindness than others People are even higher in diabetes.

The problem is particularly serious in South Asian countries. As of 2015, India has more than 70 million people with diabetes, and because of lifestyle, genetic factors, lack of doctors and adequate medical resources and other social reasons, the next 20 years the situation is very worrying to 2040 countries in South Asia, diabetes The number will grow to 140 million. But the direct issue ahead of the Indian public health sector is even more tricky: According to official statistics, there is a gap between about 120,000 ophthalmologists in the country, diabetes and diabetic retinopathy, and about 45% of patients are Before the diagnosis has been lost part or all of the vision & hellip;

Lily Peng & middot;Is Google's research institutionsGoogle ResearchA researcher. On the eve of the upcoming Google Annual Developers Conference I / O 17, she introduced us to an exciting research project: early use of machine learning techniques to detect diabetic retinopathy, timely and even prophylactic treatment, In 3 years, 5 years or even 10 years after the loss of vision of the people, get a valuable opportunity for early treatment.

"Our mission: to use depth learning techniques to train an algorithm that automatically diagnoses potential lesions from the patient's retinal fundus photographs. "She said. The task logic sounds simple, but it is not the case, because the process of training this algorithm is the key. In order to provide high-quality training materials, researchers found 54 US Food and Drug Administration (FDA) certified ophthalmologists and professionals, from May to December 2015 for a total of 128,175 retinal fundus photo material Marking and rating, and ultimately marking more than 880,000 confirmed symptoms.

Next, the neural network technology should come in handy. Lili & middot; Peng's team built a 26-story Deep Convolutional Neural Network and then trained with a marked material.


This kind of neural network structure is more special, its characteristic is for the two-dimensional structure of the data & mdash; that is the picture & mdash; & mdash; has a good performance, it is often used to learn a large number of pictures.

In January and February 2016, Google Research recruited two different ophthalmology professional retinal fundus photo library, so that the algorithm and ophthalmologist a higher. The result of this attempt is significant: the algorithm is higher than the score of the person in finding the sensitivity of the symptoms (98.8) and judging the accuracy of the symptoms (99.3) (the score is called F-score, The doctor's score is 0.91, and the algorithm got 0.95).


In the same year, thisresearch reportPublished in the American Medical Association of professional journals JAMA, access to the medical profession a lot of praise. Harvard Medical School of Andrew & middot; Bim and Isaac & middot; Kehan ​​said, "This study shows the appearance of the medical new world. & Rdquo;

The combination of computer science and medicine, even reached an unexpected effect.

Of course, this is not the first time that computer science has a valuable intersection with medicine, and even the medical profession has been familiar with the term "machine learning". In fact, in the past few decades, medical researchers have been using machines to learn this more advanced technology to try to overcome only a large amount of calculation can solve the medical problems. But with the recent years to calculate the performance of the leap-style breakthrough, the machine learning subset of the depth of learning technology began popular & mdash; no doubt, the latter will become the latest weapon in the hands of medical researchers.

Who is also familiar with the depth of learning, this interdisciplinary ability to make her particularly attention, but how does she look at the relationship between medicine and computer? In fact, not all of the medical problems have to learn to solve the machine, such as hand washing this thing and my more important task is to help my team to find those machines to learn the problem can be solved to help them understand our training data The & Rdquo;


Lily & middot; Peng explained her project

She believes that machine learning is a good tool for medical aid and mdash; used to assist doctors to make a diagnosis, rather than to determine the diagnosis. It is also true that the Google Research team is still cautious about the advancement of the technology, and Lilly & Middot; Peng has made it clear that the study is only to prove the path through machine learning to solve the problem, the results are significant and predictable. However, the process of computer diagnosis, has not yet reached the absolute degree of scientific and reliable. In the final analysis, they just know that the computer can make an accurate diagnosis, and do not fully understand why it can make an accurate diagnosis.

In fact, the problem has returned to the depth of learning technology, a core debate: regardless of recognition of the image, understand the voice, neural network technology can always output some very good results, but still no one to explain the clear, in the end it is how to do it. Some of the depth of the study experts have told me that the neural network nodes and levels, the simulation is the human brain neurons (neuron) between the connection and the hierarchy (hierarchy) thinking mode, but other brain scientists have pointed out to me, And even they did not completely figure out how the human brain in the end thinking. So you can say that the current computer network structure is not so much in the simulation of the human brain, in fact, more like a gourd painting scoop.


It seems that the problem can be argued endlessly, but the debate may happen more in academia. Fortunately, Google has been able to confirm the use of this technology to diagnose diabetic retinopathy is effective. Next, Google Research and Nikon and other ophthalmic instruments / medical service agencies to promote this technology. Further, they hope for this technology to obtain the FDA and the authority of the Indian authorities certification, so that the world's vision is threatened by diabetes, people can early diagnosis and early treatment.

They found that the fact that the diagnosis of late this situation, not only in India, in the United States and even the world is a problem, although the reasons are not the same.

In the United States, many cases are people who have submitted their own information (Note: fundus scanning) to the medical institutions waiting to check. But the time is long, people move, and change the phone, when the medical institutions to diagnose the disease, the patient was lost, "Lily & middot; Peng said, and machine learning check the biggest advantage is that you can spot the results. The research team is also trying to set up a website for users to submit their own eye scan photos for analysis & mdash; although this is not a professional diagnosis, but still enough to 5 years or even 10 years ahead of time to save the ordinary people, the future blind The

As mentioned earlier, Lily & middot; Peng's share will take place the day before I / O17. In the roundtable discussion agenda for the whole day, I've heard that Google researchers are not looking at new technologies for the next 50 or even 100 years, but 10 years is a very worthwhile node. " Similar to the expression.

Indeed, we can not explain why the neural network in the end is so powerful, but we can still use it to do some very good, in 10 years can help us things. Cutting-edge technology is actually the case, the core principles do not engage in clear, does not prevent us to use it to improve life. It was like our ancestors did not know which day suddenly discovered the wood or the impact of the stone can take fire, when they failed to study what is clear in the end, but the human or from the era of drinking blood to farming civilization took an important step.

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