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While the use of artificial intelligence (AI) for medical image analysis is gaining wide acceptance, the expertise, time and cost required to generate annotated data in the medical field are significantly high, due to limited availability of both data and expert annotation. Strongly supervised object localization models require data that is exhaustively annotated, meaning all objects of interest in an image are identified. This is difficult to achieve and verify for medical images. We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. For the strongly supervised model, this approach overcomes the need for exhaustive annotation on real images. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images. The results are demonstrated on images of human urine samples obtained using microscopy.

Artificial intelligence may help to make sporting competitions cleaner and fairer in the future. Professor of Business Informatics Wolfgang Maaß and his teams at Saarland University and the German Research Center for Artificial Intelligence are using self-learning computer systems to make it faster and simpler to uncover doping violations. Maaß and his team have been collaborating with the World Anti-Doping Agency WADA on research projects that use AI systems the team had previously developed for Industry 4.0 applications. By feeding these systems with data from doping tests, the systems become increasingly efficient at detecting sporting fraud. Unequal chances, unfair competition, unclean sport – doping doesn't just violate the principle of fairness, sportsmen, and women who use performance-enhancing substances are putting their own health on the line.

Professor of Business Informatics Wolfgang Maaß (photo) and his teams at Saarland University and the German Research Center for Artificial Intelligence are using self-learning computer systems to make it faster and simpler to uncover doping violations. Maaß and his team have been collaborating with the World Anti-Doping Agency WADA on research projects that use AI systems the team had previously developed for Industry 4.0 applications. By feeding these systems with data from doping tests, the systems become increasingly efficient at detecting sporting fraud. Artificial intelligence may help to make sporting competitions cleaner and fairer in the future. Professor of Business Informatics Wolfgang Maaß and his teams at Saarland University and the German Research Center for Artificial Intelligence are using self-learning computer systems to make it faster and simpler to uncover doping violations.

Artificial intelligence may help to make sporting competitions cleaner and fairer in the future. Professor of Business Informatics Wolfgang Maaß and his teams at Saarland University and the German Research Center for Artificial Intelligence are using self-learning computer systems to make it faster and simpler to uncover doping violations. Maaß and his team have been collaborating with the World Anti-Doping Agency WADA on research projects that use AI systems the team had previously developed for Industry 4.0 applications. By feeding these systems with data from doping tests, the systems become increasingly efficient at detecting sporting fraud. Unequal chances, unfair competition, unclean sport – doping doesn't just violate the principle of fairness, sportsmen and women who use performance-enhancing substances are putting their own health on the line.

An early, accurate cancer diagnosis can dramatically improve a patient's outcome, but the tests used to diagnose some cancers are invasive at best and downright awful at worst. Prostate cancer is one of the most common cancers in men, with more than 1.2 million new cases every year. Doctors usually screen for it by looking at the levels of a protein called prostate-specific antigen (PSA) in a patient's blood. However, the PSA test isn't very accurate (70% of the people it flags as having prostate cancer don't), so people with high levels of PSA in their blood have to undergo a biopsy to confirm the diagnosis -- an invasive procedure that can lead to rectal bleeding, difficulty urinating, and other unpleasant side effects. Now, researchers at the Korea Institute of Science and Technology (KIST) have developed an AI that can make a nearly 100% accurate prostate cancer diagnosis from a urine sample -- and it may work for other types of cancers, too.

For the past 10 years, Sonia Grego has been thinking about toilets – and more specifically what we deposit into them. "We are laser-focused on the analysis of stool," says the Duke University research professor, with all the unselfconsciousness of someone used to talking about bodily functions. "We think there is an incredible untapped opportunity for health data. And this information is not tapped because of the universal aversion to having anything to do with your stool." As the co-founder of Coprata, Grego is working on a toilet that uses sensors and artificial intelligence to analyse waste; she hopes to have an early model for a pilot study ready within nine months.

Most people consider smell their least important sense, surveys suggest. Dogs, however, feel their way through the world with their noses. Humans already employ the animals' olfactory acuity for contraband and explosives detection. More recently it has also proved uncannily good at sensing cancers, diabetes--and even COVID-19. Exactly how dogs detect diseases is a mystery, but that has not stopped researchers from mimicking this prowess with an artificial-intelligence-based noninvasive diagnostic tool.

Dogs that can smell prostate cancer could inspire'robotic noses' to sniff out the disease, in a technique dubbed'machine olfaction', a new study reveals. In a pilot study, British and US researchers trained dogs to detect aggressive prostate cancer from people's urine samples. Dogs have an extremely sensitive sense of smell and can pick up on'volatile organic compounds' (VOCs) released during the early stages of many cancers. The scientists then used the data to create an artificial neural network that could detect the cancer-specific chemicals that the dogs could smell. The hope is that the dogs' performance can eventually be replicated and used in technology such as an app on a smartphone.

If you're a hospital exec, departmental lead, or run a clinical service, you've likely been approached by a gazillion AI vendors with all sorts of shiny new tech that's just bursting with promise. If so, I know the feeling. My inbox is full of the stuff every single day. To the uninitiated it can be hard to discern which ones are actually going to help (if at all), which are fads and which are just plain dangerous. The wheat needs careful separating from the chaff.