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Diagnostic Medicine


Artificial Intelligence in Medical Imaging Market Seeking Excellent Growth

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Breakthrough in safety-critical machine learning could be just the beginning

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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 United States of Amazon and its Flywheel Economy

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"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.


Don't call it AI (but it's still innovative)

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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 releases the InnerEye Deep Learning Toolkit to improve patient care

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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.


Artificial intelligence detects osteoarthritis years before it develops

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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.


Using Machine Learning in Patient Diagnoses – Voices

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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.


NVIDIA Open Sources MONAI, An AI Framework For Medical Imaging

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NVIDIA open sources MONAI (Medical Open Network for AI), a framework developed by NVIDIA and King's College London for healthcare professionals using best practices from existing tools, including NVIDIA Clara, NiftyNet, DLTK, and DeepNeuro. Using PyTorch resources, MONAI provides domain-optimized foundational capabilities for developing healthcare imaging training in a standardized way to create and evaluate deep learning models. The MONAI framework is the open-source tool based on Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.


Aidoc Adds $20M To Series B For AI-based Radiology Tools

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Catching abnormalities on a medical image is important, but case backlogs often mean radiologists are cut short on how long they can spend with each one. Enter Aidoc, a 4-year-old Israel-based startup providing artificial intelligence tools for radiologists. The company secured an additional $20 million for its Series B funding led by Square Peg Capital, which initially led the round that began in April 2019. The new funds bring the Series B round to $47 million and gives Aidoc a total of $60 million raised to date, according to Crunchbase data. If the AI detects something, the tools alert the radiologist, Aidoc co-founder and CEO Elad Walach told Crunchbase News. "What has happened in recent history is that scanners have become cheaper, so now there is more imaging, which is overloading a radiologist's workflow," he said.


Multiple Instance Learning

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When it comes to applying computer vision in the medical field, most tasks involve either 1) image classification for diagnosis or 2) segmentation to identify and separate lesions. However, in pathology cancer detection, this is not always possible. Obtaining labels is time-consuming and labor-intensive. Furthermore, pathology slides can be up to 200k x 100k pixels resolution, and they will not fit in memory for classification since for example, the ImageNet only uses 224 x 224 pixels for training. Downsampling normally is not an option because we are trying to detect a tiny area, such as a cancerous area varying from 300 x 300 pixels area (a few dots in Figure 1).