While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.

We present a method for jointly modeling power generation from a fleet of photovoltaic (PV) systems. We propose a white-box method that finds a function that invertibly maps vector time-series data to independent and identically distributed standard normal variables. The proposed method, based on a novel approach for fitting a smooth, periodic copula transform to data, captures many aspects of the data such as diurnal variation in the distribution of power output, dependencies among different PV systems, and dependencies across time. It consists of interpretable steps and is scalable to many systems. The resulting joint probability model of PV fleet output across systems and time can be used to generate synthetic data, impute missing data, perform anomaly detection, and make forecasts. In this paper, we explain the method and demonstrate these applications.

TUSTIN, Calif., February 07, 2022--(BUSINESS WIRE)--Tarry Singh joins Marktechpost as an Advisory Board Member. Tarry Singh is Chairman, CEO, and AI Researcher of AI company Real AI Inc and deepkapha AI Research Lab. Tarry has over 25 years of experience working with data and has advised CxOs of global organizations to set up data-driven organizations from scratch. Tarry invests in AI and Web3 startup and scaleup ventures, speaks regularly at global AI leadership summits worldwide, and conducts workshops on a regular basis with his team of capable scientists. This press release features multimedia.

WIRE)--Imtiaz Adam joins Marktechpost as an Advisory Board Member. Imtiaz Adam (MBA, MSc) is a leading AI influencer and hybrid Data Science and business strategy specialist. Imtiaz is the founder of an AI startup, Deep Learn Strategies Limited (DLS). Imtiaz has been a speaker at major events such as during the WEF at Davos where he spoke at a panel on AI. He is a Sloan Fellow in Strategy from London Business School with an EMBA exchange at Columbia Business School.

TUSTIN, Calif., May 2, 2020 /PRNewswire-PRWeb/ -- Nigel Willson joins Marktechpost.com Nigel is a Global Speaker, Influencer, and Advisor on Artificial Intelligence and Co-founder of We and AI. He is ranked amongst the top AI Influencers in the World and as Co-Founder of We and AI (a non-government organization) is working to raise awareness of the risks and rewards of AI and helping to give humanity a voice in the age of machines. Marktechpost.com is a California-based Artificial Intelligence platform for the latest updates in machine learning, deep learning, and data science research. The theme of the platform is set in such a way that AI and Data Science professionals can share their knowledge and suggestions with the AI and Data Science aspirants.

WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T MR system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medical's CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction. AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. "AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medical's leadership and commitment to innovation in diagnostic imaging," said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. "With the expansion of this unique DLR method across modalities and into MR, we're elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before." Canon Medical Systems USA, Inc., headquartered in Tustin, Calif., markets, sells, distributes and services radiology and cardiovascular systems, including CT, MR, ultrasound, X-ray and interventional X-ray equipment.

Combining the power of Canon Medical's Advanced intelligent Clear IQ Engine (AiCE) with Deep Learning Spectral Reconstruction imaging capabilities, Canon Medical Systems USA, Inc. introduces the Aquilion ONE / PRISM Edition, a spectral CT system designed for deep intelligence. The advanced system integrates artificial intelligence (AI) technology to maximize conventional and spectral CT capabilities and automated workflows while providing intelligent clinical insights to assist physicians in making more informed decisions across the patient's care cycle. The Aquilion ONE / PRISM Edition offers opportunity for innovation within medical imaging with the power to illuminate clinical insights and initiate business opportunities designed to improve patient outcomes. "The intelligent technologies that make up the Aquilion ONE / PRISM Edition give healthcare providers the clinical confidence they need to reach new heights – from both a clinical and business perspective," said Erin Angel, managing director, CT Business Unit, Canon Medical Systems USA, Inc. "Canon Medical's deep learning reconstruction technology is pushing routine diagnostic imaging into the age of AI assisted imaging, revolutionizing patient care by enabling improved diagnostic confidence. We are committed to delivering products that aren't just a glimpse into the future of imaging - they are the future of imaging."

WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced Intelligent Clear-IQ Engine (AiCE) for the Aquilion PrecisionTM further expanding access to its new deep convolutional neural network (DCNN) image reconstruction technology. This technology, now available on both the Aquilion Precision and Aquilion ONE / GENESIS EditionTM premium CT systems, uses a deep learning algorithm to differentiate signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for CT image reconstruction. Aquilion Precision - the world's first Ultra-High Resolution CT provides 2 times the resolution of conventional CT, revealing detail that is typically only seen in Cath labs. With AiCE, the system now enables clinicians to perform super-high resolution studies at doses equivalent to standard resolution CT (with traditional hybrid iterative reconstruction techniques). AiCE learns from the high image quality of Model Based Iterative Reconstruction (MBIR) to reconstruct CT images with improved high contrast spatial resolution*.

Toyota's latest autonomous test vehicle was developed by Toyota Research Institute, the carmaker's Silicon Valley advanced tech unit. Automated vehicles capable of taking over driving duties from humans like robotic chauffeurs are coming, though exactly when they get here remains fuzzy. In the runup to that, Toyota wants to leverage the same artificial intelligence and advanced sensors that self-driving cars rely on for a system it calls "Guardian" to achieve something equally remarkable: Cars that can't crash. "With a Guardian vehicle the palette of things the car can do would be way more than just using the steering wheel and stepping on the brake," Ryan Eustice, vice president of autonomous driving for Toyota Research Institute, told Forbes at a recent briefing in Sonoma, Calif. "Imagine going through an intersection and you're going to get T-boned. The right thing for the car to do is accelerate you out of that. That requires a huge amount of understanding on the car's part to be able to safely do that."