Image Processing

The Ministry Human Resource Department, in a press release, said that several national tech universities in the country have set up AI centres for education and research and development. These universities include the Indian Institutes of Technology in Kharagpur and Madras and the Indian Institute of Information Technology Design and Manufacturing in Kancheepuram. Also involved are the National Institute of Technology in Silchar and the National Institute of Technology in Bhopal. Their centres will offer courses related to AI, for example, in deep learning foundations and applications, reinforcement learning, probabilistic reasoning, predictive and prescriptive data analytics, system identification, physical cybersecurity, and digital image processing. India's acts and statutes that govern these institutions allow them to freely collaborate with institutions and universities across the world for academic and research.

In Part I, we saw a few examples of image classification. In particular counting objects seemed to be difficult for convolutional neural networks. After sharing my work on the fast.ai Now we can create a learner and train it on this new dataset. Wow! Look at that, this time we're getting 100% accuracy.

The rise of AI in medical imaging has paved way for the improvement of workflow standardization, consistency and dependability imaging providers need in order to achieve the best patient care. However, as when implementing any new kind of technology into clinical workflows, there are challenges. In a special report published Jan. 30 in the inaugural issue of Radiology: Artificial Intelligence, Luciano M. Prevedello, MD, and chief of imaging informatics at The Ohio State University Wexner Medical Center in Columbus, Ohio, and colleagues recognize these challenges, but also offer potential solutions--specifically image-based competitions--which could foster collaborative AI research. The authors noted that although AI holds exciting opportunities for medical imaging, challenges related to data complexity, data access and curation, patient privacy, transferability of algorithms to mass markets and the integration of AI in clinical workflows must be addressed first in order to effectively bring AI to the forefront of augmenting patient care. "Readily available, well-curated, and labeled data of high quality is paramount to performing effective research in this area," Prevedello et al. wrote.

What's New: Non-profit RESOLVE's* new TrailGuard AI* camera uses Intel-powered artificial intelligence (AI) technology to detect poachers entering Africa's wildlife reserves and alert park rangers in near real-time so poachers can be stopped before killing endangered animals. TrailGuard AI builds on anti-poaching prototypes funded by Leonardo DiCaprio Foundation and National Geographic Society. "By pairing AI technology with human decision-makers, we can solve some of our greatest challenges, including illegal poaching of endangered animals. With TrailGuard AI, Intel's Movidius technology enables the camera to capture suspected poacher images and alerts park rangers, who will ultimately decide the most appropriate response." How It Works: TrailGuard AI uses Intel Movidius Vision Processing Units (VPUs) for image processing, running deep neural network algorithms for object detection and image classification inside the camera.

In 1933, a chicken keeper and amateur photographer decided to find the culprit who was stealing his eggs. Since its inception, security cameras are everywhere nowadays, most of the claimed "smart ones" work by streaming videos back to a monitor or a server so as someone or some software can analyze video frames and hopefully find some useful information from them. They consume a large amount of network bandwidth and power to stream videos even though ten image frames are all we need to know who was stealing the eggs. They are also facing a dilemma of out of service when the network is unstable, images cannot be analyzed and the "smart" becomes "dumb". Edge computing is a network model which enables data processing occurs at the edge of the network where the camera is located, eliminating the need to send videos to a central server for processing.

For healthcare professionals, this requires the clearest x-ray images possible to facilitate fast and accurate diagnosis of patients. Drawing on longstanding experience in the development of radiology and imaging solutions, technologies delivered by Thales provide hospitals with exceptional image quality, rapid image acquisition and processing, coupled with connectivity capabilities that enable instant clinical data transmission. New-generation solutions go further by capitalizing on synergies in artificial intelligence, cybersecurity, big data and data management, and the Internet of Things. "Our ongoing research that looks to embed artificial intelligence and deep learning into our solutions allows for a better diagnosis so that the patient is treated quickly and correctly," says Inès Mouga, Strategy & Innovation Director. "We provide healthcare professionals with unparalleled visual support through the sharpest-ever radiological image detection and interpretation solutions. With increasing amounts of data flowing given the digitalization of the health sector, we are also providing customers with the innovative cybersecurity solutions they need to protect their radiology systems."

The applications of artificial intelligence (AI) have remarkably transformed the healthcare industry. AI in the healthcare is making hype because its capacity to fetch information, process it and conclude it without direct human input. AI accomplishes the functions faster and without making errors. AI in this industry is used to analyze diseases or treatments techniques and patients' outcome. The advanced AI-driven technologies provide unique solutions, which eliminate mundane tasks, mitigate risks, and improve the efficiency of the chain of the process.

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Fluorescence microscopy usually involves a trade-off between producing a quality image and having a healthy sample. Illuminating the sample with higher laser power strengthens the fluorescent signal but risks damaging biological samples and photobleaching fluorescent dyes. Imaging at a slower frame rate with lower laser power often produces high-quality images but sacrifices information in samples that move. When such compromises hinder the recording of high-quality images, researchers often try to improve the images after the fact. To that end, Loïc Royer at the Chan Zuckerberg Biohub in San Francisco and Martin Weigert, Florian Jug, and Eugene Myers at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, have developed content-aware image restoration (CARE), a convolutional neural network trained on features specific to the system being observed.

Researchers have developed a new way to image the brain with unprecedented resolution and speed. Using this approach, they can locate individual neurons, trace connections between them, and visualize organelles inside neurons, over large volumes of brain tissue. The new technology combines a method for expanding brain tissue, making it possible to image at higher resolution, with a rapid 3-D microscopy technique known as lattice light-sheet microscopy. In a paper appearing in Science Jan. 17, the researchers showed that they could use these techniques to image the entire fruit fly brain, as well as large sections of the mouse brain, much faster than has previously been possible. The team includes researchers from MIT, the University of California at Berkeley, the Howard Hughes Medical Institute, and Harvard Medical School/Boston Children's Hospital.