Can you imagine that every day, three large planes full of passengers fall into the death of all the members? This sounds terrible, but the number of people who have been killed by malaria every year is as high as 600,000 to 800,000, which is equivalent to the probability of such a crash. Although malaria has almost been eradicated in developed regions, malaria is still a disaster in some underdeveloped regions, and the lack of adequate professional pathologists in the challenge of dealing with malaria is a major challenge. It is difficult for patients to get timely diagnosis and treatment.
At present, the technology that Microsoft is developing can help determine whether a patient is infected with malaria, which type of malaria is infected, and which channels may be infected. Compared with the traditional method, it requires a lot of manpower to look at the sample and analyze it. This technology has greatly improved the efficiency of doctors. Even in areas where medical staff are scarce, it can be less of a stretch.
Therefore, the combination of computer and medical care is far more than smart bracelets, blood glucose meters, or intelligent hardware such as Xbox and HoloLens that may be associated with medical treatment. The coverage ranges from front-end equipment to back-end systems to hidden in the end. Each type of algorithm can be an independent discipline. In fact, within Microsoft, there are close to 100 medical-related projects, both of which are very forward-looking and have already entered the practical application level.
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Zhang Yiwei, Associate Dean of Microsoft Research Asia
In my opinion, the progress of computers in the medical field is actually based on the same foundation, that is, "data change medical care". Whether it is Chinese medicine or Western medicine, it is essentially practical science. Doctors have summed up and counted the laws through countless times of practice, and finally achieved the effect of saving people from illness and illness. As the ability of humans to collect, process, and analyze data grows with the development of technologies such as cloud computing, big data, machine learning, and the Internet of Things, the ability to use big data like a doctor to analyze or aid in analyzing a disease will naturally Increasingly.
Artificial intelligence helps advance precision medicine
Cancer has always been one of the most difficult medical problems that humans need to solve urgently. Because each patient of the same type of cancer has different performances, it can be said that each patient's cancer is an independent disease, even if the doctor is rich. Experience is also difficult to make 100% accurate analysis and judgment, let alone relatively personalized precision medicine. Therefore, Microsoft Research Asia has been using digital medical image recognition as one of the main directions, and hopes to accelerate the promotion of precision medicine through the latest technology in the field of computer vision.
Since 2014, the team of Microsoft Research Asia has begun to study the identification and judgment of pathological sections of brain tumors, and to assist in the analysis and judgment of the stage of cancer in the patient through the shape, size and structure of the cells. In the past two years, we have made two breakthroughs in this field based on the "neural network + deep learning" model:
First, image processing for large-scale pathological sections is achieved. Usually the size of the picture is 224 * 224 pixels, but the size of the pathological section of the brain tumor has reached 200,000 * 200,000, or even 400,000 * 400,000 pixels. For the identification system of large-scale pathological slice images, we have not used the digital medical image database commonly used in the industry. Instead, we use as many pictures as possible on the basis of ImageNet, the most mature image database in the computer field, through our own neural network and The deep learning algorithm is continuously trained in a large amount, and finally the image processing of large-scale pathological slices is realized.
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The process of processing a large-scale pathological slice image through a neural network and a deep learning algorithm
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