AI and the Wolfram Language Work toward Partial Automation in the Search for Cancer

May 26, 2020 —Sjoerd斯密特,技术顾问,欧洲钨manbet万博app

AI and the Wolfram Language Work toward Partial Automation in the Search for Cancer

NOTE: The following post contains real medical images.

As more technology is folded into medical environments all over the world, Wolfram’s European branch has taken on work with the United Kingdom’s国家健康服务(NHS) in an effort to partially automate the process of cancer diagnosis. The task is to use machine learning to avoid checking thousands of similar-looking images of people’s insides by hand for signs of cancer.



例如:有名,在1873年的业余数学家William Shanks(1812–1882) had calculatedπ达到了前所未有的707位小数,计算数学常数是他的爱好。不幸的是,当一个机械计算器使用71年后检查他的结果,事实证明,只有前527是正确的。如果甚至有人为高度激发的小腿可以使重复任务的失误,任何人都可以。

Computingπis something we can safely let a computer handle because it will always outperform a human. However, some jobs can only be automated with machine learning algorithms, which cannot guarantee correct results. So we’re back to the dilemma we started with: what do we do with tasks that are both very important and very tedious?


在一个Wolfram Technical Services与NHS,其服务提供商项目CorporateHealth International通过拨款资助Innovate UK- 我们正在探索的方式,审查的人内部的视频,以检查他们肠癌的迹象。这些视频是由弹丸摄像机,通过你的消化系统传播,并且不断地发送图片到你在你的身上穿的记录作出。该程序,在此解释video about the HI-CAP Projectand anothervideo about data science in endoscopy,是显著更容易,更便宜,比去医院有医生闲逛你的内心与内窥镜更舒适。出于这个原因和其他人,它具有早期检测肿瘤时,他们仍然可以很容易地处理,以挽救许多生命的潜力。

The ease of gathering the data does not directly translate to ease of analysis. Each video consists of thousands of frames, and some polyps or tumors will only appear on a single frame and may not even stand out from the background all that much. This means that a small army of nurses—employed by CorporateHealth International—is currently needed to analyze every single frame of each video, which is a laborious process, as you can imagine.

To alleviate this workload, we work together with the计算机视觉组from the巴塞罗那大学,其中神经网络正在为息肉鉴定正是这项任务的发展。目前,该网络已实现TensorFlow,但我们计划将它移植过来的沃尔夫勒姆manbet万博app神经网络架构(using some intermediary format likeONNX) to make it part of a larger data-processing pipeline for pill camera videos.

Trusting AI Results

是不够简单培训网络和测试工程师t it on a validation set before it can be put into practice. If the people who actually have to review the videos (and therefore bear responsibility for that analysis) are not convinced of the quality of the computer’s results, they will double-check everything by hand regardless, or even just return to the tools they are currently using. You can’t blame them for wanting to be thorough.

For this reason, we are experimenting with different ways to present computer results to nurses, allowing corrections where necessary. This means playing around with the order in which the frames are presented (e.g., chronological vs. ordering by classification); how the computer classification is presented (a number, a class, a heat map on the image, etc.); and what kind of actions the nurse can take to correct the result so it can then be fixed in the next training round of the AI.

An example of AI identification of polyps

An example of AI identification of polyps (Figure 8 fromthis paper).

我们的目标是使用manbet万博app沃尔弗拉姆动态交互语言to build a tool that allows users to slowly build experience in such a way that they start trusting AI results more and more—in particular, the parts of the video where a computer indicates no risk factors. If a few frames are unjustly highlighted as polyps because it’s a little overcautious, it’s not much work to correct the result manually. On the other hand, if the AI tells the user that 99% of the video is free of polyps and the user doesn’t trust that verdict, they will still check the entire video and the addition of an AI to the process will not have saved much time at all.




在像息肉检测复杂的任务,计算机不能提供完全权威的计算类似的数字π; their role is closer to that of a second opinion from another specialist. Unlike other specialists, though, we cannot directly communicate with a computer and ask it why it made a certain decision. The computer is a sort of “silent expert,” if you will. While the technology is promising, it is still a work in progress with questions yet to be explored. The best we can do is to interrogate the internals of the neural network to try and understand how it works, making it important to think carefully about how this silent expert is incorporated into a decision-making process that ultimately affects people’s lives.

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