Medical image annotation is a major hurdle for developing precise and robust machine learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points utilizing the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks.
The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism. RSNA collaborated with the Society of Thoracic Radiology (STR) to create a massive dataset for the challenge. The RSNA-STR Pulmonary Embolism CT (RSPECT) dataset is comprised of more than 12,000 CT scans collected from five international research centers. The dataset was labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.
September 23, 2020 – An artificial intelligence algorithm can detect subtle signs of osteoarthritis in MRI scans, years before symptoms of the condition even begin. Researchers at University of Pittsburgh School of Medicine and Carnegie Mellon University College of Engineering noted that right now, the primary treatment for osteoarthritis is joint replacement. The condition is so prevalent that knee replacement is the most common surgery in the US for people over the age of 45. "The gold standard for diagnosing arthritis is x-ray. As the cartilage deteriorates, the space between the bones decreases," said study co-author Kenneth Urish, MD, PhD, associate professor of orthopaedic surgery at Pitt and associate medical director of the bone and joint center at UPMC Magee-Womens Hospital. "The problem is, when you see arthritis on x-rays, the damage has already been done. It's much easier to prevent cartilage from falling apart than trying to get it to grow again."
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Safety is the central focus on driverless vehicle systems development. Artificial intelligence (AI) is coming at us fast. It's being used in the apps and services we plug into daily without us really noticing, whether it's a personalized ad on Facebook, or Google recommending how you sign off your email. If these applications fail, it may result in some irritation to the user in the worst case. But we are increasingly entrusting AI and machine learning to safety-critical applications, where system failure results in a lot more than a slight UX issue.
"The tech giants have as much money and influence as nation states." Tech Giants include Apple Facebook, and Google ... but Amazon's unique flywheel makes it the torchbearer. "AWS alone is on track to be worth $1 trillion." The Amazon flywheel fuels a circular, data-driven ecosystem that's bolstered by Open Innovation. This article summarizes two from a series called the Tech Nations project.
AI has long been portrayed as both an opportunity and a threat to humankind. We've seen AI medical image processing identify a large volume of eye-patients' macular degeneration, and we've seen deep learning trawl astral data searching for habitable exoplanets. Yet the negative hype around AI would have you believe that we're heading straight for a Terminator-style apocalypse. The reality of course, is far less extravagant. Today there are plenty of systems that are AI-led, like convolutional neural networks for example, which can be used to identify faces in a picture.
Microsoft's Project InnerEye has been involved in building and deploying machine learning models for years now. The team has been working with doctors, clinicians, oncologists, assisting them in tasks like radiotherapy, surgical planning, and quantitative radiology. This has reduced the burden on the people involved in the domain. The firm says that the goal of Project InnerEye is to "democratize AI for medical image analysis" by allowing researchers and medical practitioners to build their own medical imaging models. With this in mind, the team released the InnerEye Deep Learning Toolkit as open-source software today.
Researchers at the University of Pittsburgh School of Medicine and Carnegie Mellon University College of Engineering have created a machine-learning algorithm that can detect subtle signs of osteoarthritis--too abstract to register in the eye of a trained radiologist--on an MRI scan taken years before symptoms even begin. These results will publish this week in PNAS. With this predictive approach, patients could one day be treated with preventative drugs rather than undergoing joint replacement surgery. "The gold standard for diagnosing arthritis is X-ray. As the cartilage deteriorates, the space between the bones decreases," said study co-author Kenneth Urish, M.D., Ph.D., associate professor of orthopaedic surgery at Pitt and associate medical director of the bone and joint center at UPMC Magee-Womens Hospital.
When I was six years old, I remember walking with my father to the doctor's office, which was in a clinic two towns from where we lived. When we reached the Afari clinic, the only nurse on duty recorded my vital symptoms, including my temperature, pulse, and blood pressure, and told us to wait for our turn. I was the 30th person in line to meet the only doctor available at the clinic. We waited for hours before it was finally my turn. The doctor went over my vital symptoms which were: Pressure: Normal; Temperature: High; Pulse: Normal.