Will doctors also lose their jobs? Chinese companies develop artificial intelligence technology to diagnose cancer

NVIDIA actively cultivates deep learning development talents and provides practical training courses in cooperation with industry, government, and academia. So can't other chip vendors, such as Xilinx, which also promotes the use of its FPGAs in deep learning, offer similar training programs?

Kevin Krewell, principal analyst at market research firm Tirias Research, believes that this is not always the case: "FPGAs are still too complex for machine learning programming, and there are some advantages to using FPGAs (or designing their own ASICs like Google's TPUs), but GPUs are generally available, immediately available and versatile, and can be used to perform both display and machine learning." ”

Nvdia promotes real-world success stories of deep learning

Nvidia highlighted companies that are already developing deep learning programs/products on the company's platform, such as Infervision, a Chinese startup that aims to develop artificial intelligence tomography (CT) diagnostic solutions for lung cancer diagnostics.

Imagine that Chen Kuan (CK), founder and CEO of Imagine Technology, is himself one of the leading figures in the AI wave, and his program will show how new technology can help medical radiographers read CT scans and X-ray results to detect suspicious lesions and nodules in lung cancer patients earlier and more efficiently.

Chen Kuan did not participate in Nvidia's training, but in 2012, while majoring in economics and finance at the University of Chicago, he came across an introduction to Nvidia's deep learning platform: "A friend of mine showed it to me, and I was fascinated." ”

During the 2012 U.S. presidential election, he worked with other University of Chicago and MIT students to use AI to develop a program that could sort posts from bipartisan candidates Barack Obama and Mitt Romney on Twitter and detect public perception of candidates. This is Chen Kuan's first investment in the field of deep learning.

In 2014, Chen Kuan, a doctoral student, returned to China to look for AI business opportunities in different industries, and after several interviews, a radiation technologist working in China's top hospital offered him an inspiration on the possibility of developing deep learning cancer detection technology, which led to the birth of speculative technology. Chen Kuan can be said to have met a nobleman.

The adoption of physicians is a key factor in the continued refinement of the process of conceiving technology development, and Chen Kuan said that more than 100 hospitals in China are now working with the company to import data captured by tomography and X-ray equipment and compare the results.

The watershed moment of Chen Kuanzhi's deep learning products occurred when AlphaGo, developed by Google's artificial intelligence company Deep Mind, defeated human Go masters in 2015; AlphaGo won again in 2016 in a duel against human chess players. Chen Kuan said: "After that, the Chinese medical community who was still skeptical of AI changed their attitudes; Otherwise, no one really trusts deep learning software. ”

Physicians at Tongji Hospital in Wuhan, China, are using a program developed by Speculative Technology (Source: Speculative Technology)

Let the machine learn on its own

Chen Kuan said that doctors have been using traditional computer-aided machine vision software, such as R2, since the 1990s; But R2, unlike the new generation of deep learning software programs of Imagination Technology, the physician must first tell the machine what to look for and describe the characteristics of the object to find, although it is also the result of many experts developing, but the accuracy is not very high.

Speculative technology is about letting the machine learn what to look for: "The machine will learn the actual area to pay attention to and the characteristics of the object to look for; However, Chen Kuan stressed that such learning depends on the large amount of data collected from various medical institutions over a long period of time.

Fortunately, since the outbreak of the SARS pandemic in 2002, the Chinese government has actively promoted the installation of new generation IT equipment in large hospitals; Chen Kuan said that many first-tier hospitals already have their own data centers to store all the image data. Of course, the stored images are not perfect: "If the resolution is too poor, it becomes a classic case of GIGO (garbage in, garbage out)." ”

Imagine Technology is currently preparing to complete test results from radiographers involved in early adoption programs, and in order to expand its business, the company is also awaiting approval for its software from the China Food and Drug Administration (CFDA).

Chen Kuan said that so far, it seems that the results of comparative results between human radiographers and computers are "quite promising", and the two can find cancerous nodules larger than 6mm at the same time; The computer performs better in the search for nodules of 3~6mm or less. But he also admits that scientists can't yet explain how computers can reach specific conclusions, which is a shortcoming of deep learning.

He also stressed that deep learning software is not intended to replace radiologists, but rather that human experts work with computers to verify correct results.