This CCF-GAIR 2017 will usher in more experts in the artificial intelligence and robotics industry. CCF-GAIR provides a broad communication platform for academic and industry experts at home and abroad. It not only grasps the global artificial intelligence trend pulse at a macro level, but also deeply discusses the application practice details of artificial intelligence in each vertical field. Today I will introduce the guest speaker of the future medical field, Dr. Feng Yuan, Director of Tianqin Medical Company and Director of AI Lab.

Feng Yuan is a Ph.D. in applied mathematics at the University of the Netherlands, and a Ph.D. in computer science at Zhejiang University. She is mainly responsible for medical image big data mining and artificial intelligence models and algorithms research. Prior to joining Tianqin, she worked in the Shanghai Lian Ying Technology Radiation Division, responsible for radiotherapy software system architecture, modeling and algorithm design.

Using AI for medical image analysis can help doctors locate the condition and analyze the condition and assist in making a diagnosis. At present, more than 90% of medical data comes from medical images. Most of these data are analyzed manually. If you can use algorithms to automatically analyze images and compare them with other case records, you can greatly reduce medical misdiagnosis and help make accurate diagnosis.

Since 2016, with the craze of artificial intelligence, a number of companies have used AI technology for intelligent analysis of medical images. I have done an inventory in an article.

深度学习被神话:医疗影像分析还需多结合传统模式

Feng Yuan told the author that the characteristics of Tianqin Medical are technology-oriented, providing middleware and services, and going to market through professional equipment manufacturers and professional medical imaging companies. Depending on the needs, they develop different algorithmic modules to promote medical imaging with partners in this form.

“Our main customers are not hospitals, doctors, but medical imaging companies and medical device manufacturers. We have a universal algorithm platform for medical imaging that can quickly develop specialized systems for solving specific problems for different problems.”

It is understood that Tianqin is also engaged in research and technical research with mainstream medical equipment manufacturers, and also conducts technical research in hospitals.

Feng Yuan believes that although the company was established this year, it has started research and development since 14 and 15 years, and domestic medical image analysis is still not mature compared with the United States. It is still in its infancy and there are still many opportunities. The most obvious is that domestic companies are mostly in the stage of angels, and American companies have entered the A and B rounds.

She believes that many companies' products are auxiliary treatments for a specific organ, but Tianqin is a common platform, and customers can develop specialized algorithm modules if they need lung diagnosis services. On a common platform, it takes only a small amount of time to develop an algorithm module using an automated build model platform.

Is this general-purpose platform facing the lack of image data? After all, compared to foreign countries, domestic informationization is insufficient, and there is no standardized system to provide high-quality data.

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