Without a doubt one of the most exciting potential uses for AI (Artificial Intelligence) and in particular deep learning is in healthcare. Traditionally, diagnosis of killer illnesses such as cancer and heart disease have relied on examinations of x-rays and scans to spot early warning signs of developing problems. Image recognition is of course one of the tasks at which deep learning excels – from Facebook's facial recognition to Google's image search, practical examples of it in use are becoming more common by the day. Although being able to tag pictures of our friends without typing their name, or find amusing images of cats when we want them, may seem trivial use cases, the same technology is quickly advancing to a point where more far-reaching implications are being realized. In China, lung cancer is the leading cause of death, claiming over 600,000 lives each year, largely due to high levels of air pollution.
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.
DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. This Smart News Release features multimedia. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.
Google just killed the Captcha, perhaps the most obstructive thing on the entire internet. For years, Captcha served as the primary way of telling humans and robots apart on the internet. It made sure that the person looking to access a website was actually a human being – ensuring that robots couldn't be used to send spam or flood a website with requests, for instance. But over time, robots have gradually become too clever for the often simple tests – which early on required people to transcribe hard-to-read text. With that, the technologies have become more complex, too.
Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation. Now, the company's leading AI experts are working on systems that can do something even more complex: Read passages of text and answer questions about them. "We're trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it's written or orally," said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year. The Maluuba team is one of several groups at Microsoft that are tackling the challenge of machine reading. Two other research teams, one at the company's Redmond, Washington, headquarters and the other in its Beijing, China, research lab, are currently leading a competition run by Stanford University that uses information from Wikipedia to test how well AI systems can answer questions about text passages.