Question How does the performance of an automated deep learning algorithm compare with manual grading by ophthalmologists for identifying diabetic retinopathy in retinal fundus photographs? Finding In 2 validation sets of 9963 images and 1748 images, at the operating point selected for high specificity, the algorithm had 90.3% and 87.0% sensitivity and 98.1% and 98.5% specificity for detecting referable diabetic retinopathy, defined as moderate or worse diabetic retinopathy or referable macular edema by the majority decision of a panel of at least 7 US board-certified ophthalmologists. At the operating point selected for high sensitivity, the algorithm had 97.5% and 96.1% sensitivity and 93.4% and 93.9% specificity in the 2 validation sets. Meaning Deep learning algorithms had high sensitivity and specificity for detecting diabetic retinopathy and macular edema in retinal fundus photographs. Importance Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015.

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There is an ongoing race among semiconductor companies, including the established market heavyweights and startups alike, to define the hardware platform that will run compute-intensive deep learning algorithms quickly and efficiently. Until now, NVIDIA has dominated the deep learning market with its graphics processor unit (GPU) chips, which bring massive parallelization, however field programmable gate arrays (FPGAs) and digital signal processors (DSPs) are starting to catch up. Deep learning is largely characterized by deep neural networks (DNNs) and convolutional neural networks (CNNs), which can become massively complex. Google's cat recognition neural network back had 1 billion connections using 16,000 processors. GPUs are known to achieve the best speed and throughput, around 100x faster compared to an FPGA, while FPGAs are known to have better power efficiency, around 50x better compared to a GPU.

Google's artificial intelligence can play the ancient game of Go better than any human. It can identify faces, recognize spoken words, and pull answers to your questions from the web. But the promise is that this same kind of technology will soon handle far more serious work than playing games and feeding smartphone apps. One day, it could help care for the human body. Demonstrating this promise, Google researchers have worked with doctors to develop an AI that can automatically identify diabetic retinopathy, a leading cause blindness among adults. Using deep learning--the same breed of AI that identifies faces, animals, and objects in pictures uploaded to Google's online services--the system detects the condition by examining retinal photos.

This article was posted by Adrian Rosebrock on Pyimagesearch. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. This list is by no means exhaustive, it's simply a list of libraries that he has used in his computer vision career and found particular useful at one time or another. The goal of this blog post is to introduce you to these libraries. He encourages you to read up on each them individually to determine which one will work best for you in your particular situation.

Artificial intelligence has become a frequent topic in the news cycle, with reports of breakthroughs in speech recognition, computer vision, and textual understanding that have made their way into a bevy of products and services that are used every day. In contrast, clinical care has yet to reach the much lower bar of automating health care information transactions in the form of electronic health records. Medical leaders in the 1960s and 1970s were already speculating about the opportunities to bring automated inference methods to patient care,1 but the methods and data had not yet reached the critical mass needed to achieve those goals. The intellectual roots of "deep learning," which power the commodity and consumer implementations of present-day artificial intelligence, were planted even earlier in the 1940s and 1950s with the development of "artificial neural network" algorithms.2,3 These algorithms, as their name suggests, are very loosely based on the way in which the brain's web of neurons adaptively becomes rewired in response to external stimuli to perform learning and pattern recognition.

Living in a dynamic physical world, it's easy to forget how effortlessly we understand our surroundings. With minimal thought, we can figure out how scenes change and objects interact. But what's second nature for us is still a huge problem for machines. With the limitless number of ways that objects can move, teaching computers to predict future actions can be difficult. Recently, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have gotten a step closer, developing a deep-learning algorithm that, given still images from a scene, can create brief videos that simulate the future of that scene.

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What is Next in MLTechnology? Use Cases & Demo 1 2 3 4 5 4. Machine Learning "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" – T. Michell (1997) Example: A program for soccer tactics • Task: Win the game • Performance: Goals • Experience: (x) Players' movements (y) Evaluation 6. A few thousand years ago: Manual Plowing Today:Automated Plowing Path of Machine Evolution… 7. Automation Evolution System that Do • Replicate repetitive human actions System that Think • Cognitive capabilities handle judgment-oriented tasks System that Learn/Adapt • Learn to understand context and adapt to users and systemsRobotic Automation CognitiveAutomation IntelligentAutomation Natural Language Processing Big Data Analytics Artificial Intelligence Machine Learning Large Scale Processing Adaptive Alteration Rule Engine Screen Scraping Workflow Unstructured Data Processing (Extraction) Knowledge Modelling (Ontologies) Implementation: • Macro-based applets • Screen Scraping data collection • Workflow Implementation • Process Mapping • Business Process Management Implementation: • Built-in Knowledge repository • Learning capabilities • Ability to work with unstructured data • Pattern recognition • Reading source data manuals Implementation: • Artificial Intelligence Systems • Natural Language Understanding and Generation • Self Optimizing / Self Learning • Predictive Analytics / hypothesis generation • Evidence based learning Capabilities Capabilities Capabilities 8. Evolution of Machine Intelligence • Raw computing power can automate complex tasks!Great Algorithms Fast Computers • Automating automobiles into autonomous automata!More Data Real- Time Processing • Automating question answering and information retrieval!Big Data In- Memory Clusters • Deep Learning Smart Algorithms Master Gamer Deep Learning • New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) ImproveTraining Efficiency IBM Deep Blue Google Self Driven Cars Watson Jeopardy Deepmind Atari Game One Shot Learning 9. Why Machine Learning? Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart.

When you read the title, whether your mind immediately went for the upstairs "H" or the downstairs "H" probably says something about whether the new applications of Big Data in law enforcement let you sleep like a baby or keep you up at night. You might have thought your choice of "H" related to whether you've been on the receiving end of Big Data in law enforcement but the fact is that practically all of us have, and for those who haven't it won't take much longer to reach you. There is an absolute explosion in the use of Big Data and predictive analytics in our legal system today driven by the latest innovations in data science and by some obvious applications. It hasn't always been so. In the middle 90s I was part of the first wave trying to convince law enforcement to adopt what was then cutting edge data science.