According to the World Health Organization (WHO), 3.6 billion medical imaging tests are performed every year globally to diagnose, monitor, and treat various conditions. Most of these images are stored in a globally recognized standard called DICOM (Digital Imaging and Communications in Medicine). Imaging studies in DICOM format are a combination of unstructured images and structured metadata.
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A new study and AI model from researchers at Stanford University is streamlining cancer diagnostics, treatment planning, and prognosis prediction. Named MUSK (Multimodal transformer with Unified maSKed modeling), the research aims to advance precision oncology, tailoring treatment plans to each patient based on their unique medical data. “Multimodal foundation models are a new frontier in…
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An advanced deep-learning model that automates X-ray analysis for faster and more accurate assessments could transform spinal health diagnostics. Capable of handling even complex cases, the research promises to help doctors save time, reduce diagnostic errors, and improve treatment plans for patients with spinal conditions like scoliosis and kyphosis. “Although spinopelvic alignment analysis…
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As MONAI celebrates its fifth anniversary, we’re witnessing the convergence of our vision for open medical AI with production-ready enterprise solutions. This announcement brings two exciting developments: the release of MONAI Core v1.4, expanding open-source capabilities, and the general availability of VISTA-3D and MAISI as NVIDIA NIM microservices. This dual release reflects our…
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Prostate cancer researchers unveiled a new AI-powered model that can quickly analyze MRIs to accurately predict how prostate cancer tumors may develop and potentially metastasize over time. The technology uses a segmentation algorithm to quickly analyze MRIs of prostates and outline—in detail—the contours of any cancerous tumors. The model can then calculate the volume of the tumors it…
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A new deep learning model could reduce the need for surgery when diagnosing whether cancer cells are spreading, including to nearby lymph nodes—also known as metastasis. Developed by researchers from the University of Texas Southwestern Medical Center, the AI tool analyzes time-series MRIs and clinical data to identify metastasis, providing crucial, noninvasive support for doctors in treatment…
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Researchers at UCLA have developed a new AI model that can expertly analyze 3D medical images of diseases in a fraction of the time it would otherwise take a human clinical specialist. The deep-learning framework, named SLIViT (SLice Integration by Vision Transformer), analyzes images from different imagery modalities, including retinal scans, ultrasound videos, CTs, MRIs, and others…
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Developers in the fields of image-guided surgery and surgical vision face unique challenges in creating systems and applications that can significantly improve surgical workflows. One such challenge is efficiently combining multi-modal imaging data, such as preoperative 3D patient images with intra-operative video. This is key to providing surgeons with real-time…
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A recent study introduced a cutting-edge AI-powered pathology platform that can help doctors diagnose and evaluate lung cancer in patients quickly and accurately. Developed by a team of researchers at the University of Cologne’s Faculty of Medicine and University Hospital Cologne, the tool provides fully automated and in-depth analysis of benign and cancerous tissues, for faster and more…
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VISTA-2D is a new foundational model from NVIDIA that can quickly and accurately perform cell segmentation, a fundamental task in cell imaging and spatial omics workflows that is critical to the accuracy of all downstream tasks. The VISTA-2D model uses an image encoder to create image embeddings, which it can then turn into segmentation masks (Figure 1). The embeddings must contain…
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Synthetic data in medical imaging offers numerous benefits, including the ability to augment datasets with diverse and realistic images where real data is limited. This reduces the costs and labor associated with annotating real images. Synthetic data also provides an ethical alternative to using sensitive patient data, which helps with education and training without compromising patient privacy.
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NVIDIA Holoscan is the NVIDIA domain-agnostic multimodal real-time AI sensor processing platform that delivers the foundation for developers to build their end-to-end sensor processing pipeline. NVIDIA Holoscan SDK features include: Holoscan SDK can be used to build streaming AI pipelines for a range of industries and use cases, including medical devices, high-performance computing at…
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Genomics researchers use different sequencing techniques to better understand biological systems, including single-cell and spatial omics. Unlike single-cell, which looks at data at the cellular level, spatial omics considers where that data is located and takes into account the spatial context for analysis. As genomics researchers look to model biological systems across multiple omics at…
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This post delves into the capabilities of decoding DICOM medical images within AWS HealthImaging using the nvJPEG2000 library. We’ll guide you through the intricacies of image decoding, introduce you to AWS HealthImaging, and explore the advancements enabled by GPU-accelerated decoding solutions. Embarking on a journey to enhance throughput and reduce costs in deciphering medical images…
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Edge AI developers are building AI applications and products for safety-critical and regulated use cases. With NVIDIA Holoscan 1.0, these applications can incorporate real-time insights and processing in milliseconds. With the recent release of NVIDIA Holoscan 1.0, developers can more easily build production-ready applications for multimodal, real-time sensor processing.
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Driving the future of healthcare imaging, NVIDIA MONAI microservices are creating unique state-of-the-art models and expanded modalities to meet the demands of the healthcare and biopharma industry. The latest update introduces a suite of new features designed to further enhance the capabilities and efficiency of medical imaging workflows. This post explores the following new features…
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AI is increasingly being used to improve medical imaging for health screenings and risk assessments. Medical image segmentation, for example, provides vital data for tumor detection and treatment planning. And yet the unique and varied nature of medical images makes achieving consistent and reliable results challenging. NVIDIA MONAI Cloud APIs help solve these challenges…
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Whole human brain imaging of 100 brains at a cellular level within a 2-year timespan, and subsequent analysis and mapping, requires accelerated supercomputing and computational tools. This need is well matched by NVIDIA technologies, which range across hardware, computational systems, high-bandwidth interconnects, domain-specific libraries, accelerated toolboxes, curated deep-learning models…
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Explore how Metropolis APIs and microservices on NVIDIA Jetson can significantly reduce vision AI development timelines from years to months.
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Discover how PepsiCo, Runway, SoftServe, and AWS used GPU-accelerated SDKs for their CV applications.
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Digital pathology slide scanners generate massive images. Glass slides are routinely scanned at 40x magnification, resulting in gigapixel images. Compression can reduce the file size to 1 or 2 GB per slide, but this volume of data is still challenging to move around, save, load, and view. To view a typical whole slide image at full resolution would require a monitor about the size of a tennis…
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The analysis of 3D medical images is crucial for advancing clinical responses, disease tracking, and overall patient survival. Deep learning models form the backbone of modern 3D medical representation learning, enabling precise spatial context measurements that are essential for clinical decision-making. These 3D representations are highly sensitive to the physiological properties of medical…
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Edge AI applications, whether in airports, cars, military operations, or hospitals, rely on high-powered sensor streaming applications that enable real-time processing and decision-making. With its latest v0.5 release, the NVIDIA Holoscan SDK is ushering in a new wave of sensor-processing capabilities for the next generation of AI applications at the edge. This release also coincides with the…
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MONAI, the domain-specific, open-source medical imaging AI framework that drives research breakthroughs and accelerates AI into clinical impact, has now been downloaded by over 1M data scientists, developers, researchers, and clinicians. The 1M mark represents a major milestone for the medical open network for AI, which has powered numerous research breakthroughs and introduced new developer tools…
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See how recent breakthroughs in generative AI are transforming media, content creation, personalized experiences, and more.
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Over the past decade, the rapid development of deep learning convolutional neural networks has completely revolutionized how computer vision tasks are performed. Algorithm, software, and hardware improvements have enabled single computer vision models to run at incredibly fast speeds. This real-time performance opens up new possibilities for a wide range of applications, such as digital surgery.
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With a wide breadth of open source, accelerated AI frameworks at their fingertips, medical AI developers and data scientists are introducing new algorithms for clinical applications at an extraordinary rate. Many of these models are nothing short of groundbreaking, yet 87% of data science projects never make it into production. In most data science teams, model developers lack a fast…
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Join experts from NVIDIA and Microsoft on November 30 for the latest developments in deep learning with demos and guidance for getting started on the cloud.
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Medical imaging is an essential instrument for healthcare, powering screening, diagnostics, and treatment workflows around the world. Innovations and breakthroughs in computer vision are transforming the healthcare landscape with new SDKs accelerating this renaissance. MONAI, the Medical Open Network for AI, houses many of these SDKs in its open-source suite built to drive medical AI…
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NVIDIA FLARE 2.2 includes a host of new features that reduce development time and accelerate deployment for federated learning, helping organizations cut costs for building robust AI. Get the details about what’s new in this release. An open-source platform and software development kit (SDK) for Federated Learning (FL), NVIDIA FLARE continues to evolve to enable its end users to leverage…
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A virtual event designed for healthcare developers and startups, this summit on November 10, 2022 offers a full day of technical talks to reach developers and technical leaders in the EMEA region. Get best practices and insights for applications, from biopharma to medical imaging.
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Join us on October 24 for a deep dive into MONAI, the essential framework for AI workflows in healthcare—including use cases, building blocks, and more.
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Researchers from NYU Langone Health aim to improve breast cancer diagnostics with a new AI model. Recently published in Science Translational Medicine, the study outlines a deep learning framework that predicts breast cancer from MRIs as accurately as board-certified radiologists. The research could help create a foundational framework for implementing AI-based cancer diagnostic models in clinical…
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In the operating room, the latency and reliability of surgical video streams can make all the difference for patient outcomes. Ultra-high-speed frame rates from sensor inputs that enable next-generation AI applications can provide surgeons with new levels of real-time awareness and control. To build real-time AI capabilities into medical devices for use cases like surgical navigation…
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Developing for the medical imaging AI lifecycle is a time-consuming and resource-intensive process that typically includes data acquisition, compute, and training time, and a team of experts who are knowledgeable in creating models suited to your specific challenge. Project MONAI, the medical open network for AI, is continuing to expand its capabilities to help make each of these hurdles easier no…
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There is an abundance of market-approved medical AI software that can be used to improve patient care and hospital operations, but we have not yet seen these technologies create the large-scale transformation in healthcare that was expected. Adopting cutting-edge technologies is not a trivial exercise for healthcare institutions. It requires a balance of legal, clinical…
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It’s never been more important to put powerful AI tools in the hands of the world’s leading medical researchers. That’s why NVIDIA has invested in building a collaborative open-source foundation with MONAI, the Medical Open Network for AI. MONAI is fueling open innovation for medical imaging by providing tools that accelerate image annotation, train state-of-the-art deep learning models…
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Natural language processing (NLP) can be defined as the combination of artificial intelligence (AI), computer science, and computational linguistics to understand human communication and extract meaning from unstructured spoken or written material. NLP use cases for healthcare have increased in the last few years to accelerate the development of therapeutics and improve quality of patient…
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At the Computer Vision and Pattern Recognition Conference (CVPR), NVIDIA researchers are presenting over 35 papers. This includes work on Shifted WINdows UNEt TRansformers (Swin UNETR)—the first transformer-based pretraining framework tailored for self-supervised tasks in 3D medical image analysis. The research is the first step in creating pretrained, large-scale, and self-supervised 3D models…
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Advances in edge computing, video cameras, real-time processing, and AI have helped transform medical devices over the years. NVIDIA developed the NVIDIA Clara Holoscan platform to support the development of software-defined AI medical devices. The latest release of the NVIDIA Clara Holoscan SDK 0.2 offers real-time AI inference capabilities and fast I/O for high-performance streaming…
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Capturing clear diagnostic images of the heart and its vasculature is challenging in cardiac computed tomography (CT) imaging because the heart is always moving and the resulting images can be blurry. When a heart is beating quickly, at above 75 beats per minute or irregularly, good image resolution is almost impossible. Global diagnostic imaging leader Fujifilm Healthcare developed Cardio…
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New advances in computation make it possible for medical devices to automatically detect, measure, predict, simulate, map, and guide clinical care teams. NVIDIA Clara Holoscan, the full-stack AI computing platform for medical devices, has added new sensor front-end partners for video capture, ultrasound research, data acquisition, and connection to legacy-medical devices.
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In a medical first, a robot has performed a laparoscopic operation without the guidance of a surgeon’s hand. The study, recently published in Science Robotics, outlines the design of an enhanced version of the Smart Tissue Autonomous Robot (STAR) that completed the challenging task on the soft tissue of a pig. The accomplishment marks a milestone toward fully automated robotic surgeries.
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NVIDIA Jetson Nano is paving the way to detect certain types of cancer sooner. Adam Milton-Barker’s grandfather, Peter Moss, was diagnosed with a terminal illness, Acute Myeloid Leukemia, in 2018. One month prior, doctors had given his grandfather an ‘all clear’ during a routine blood test with no signs of leukemia. At the time, he was convinced there should have been some sort of sign about…
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A recently developed AI platform is giving medical professionals screening for breast cancer a new, transparent tool for evaluating mammography scans. The research, creates an AI model that evaluates the scans and highlights parts of an image the algorithm finds relevant. The work could help medical professionals determine whether a patient needs an invasive—and often nerve-wracking—biopsy.
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Deep learning models require vast amounts of data to produce accurate predictions, and this need becomes more acute every day as models grow in size and complexity. Even large datasets, such as the well-known ImageNet with more than a million images, are not sufficient to achieve state-of-the-art results in modern computer vision tasks. For this purpose, data augmentation techniques are…
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Federated learning (FL) has become a reality for many real-world applications. It enables multinational collaborations on a global scale to build more robust and generalizable machine learning and AI models. For more information, see Federated learning for predicting clinical outcomes in patients with COVID-19. NVIDIA FLARE v2.0 is an open-source FL SDK that is making it easier for data…
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At RSNA 2021, there are dedicated tracks on ultrasound imaging, which is a cost-effective way to see what is going on inside a patient’s body without exposure to radiation or the need for injections and surgeries. Ultrasound imaging is typically done by trained sonographers and needs special expertise to interpret. The probe is a small transducer to both transmit sound waves into the body…
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Project MONAI continues to expand its end-to-end workflow with new releases and a new subproject called MONAI Deploy Inference Service. Project MONAI is releasing three new updates to existing frameworks, MONAI v0.8, MONAI Label v0.3, and MONAI Deploy App SDK v0.2. It’s also expanding its MONAI Deploy subsystem with the MONAI Deploy Inference Service (MIS), a server that runs MONAI…
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NVIDIA Clara Holoscan provides a scalable medical device computing platform for developers to create AI microservices and deliver insights in real time. The platform optimizes every stage of the data pipeline: from high-bandwidth data streaming and physics-based analysis to accelerated AI inference, and graphic visualizations. The NVIDIA Clara AGX Developer Kit, which is now available…
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Mount Sinai researchers have created a new AI technology that can identify small changes within the heart, and accurately predict heart failure. Recently published in the Journal of the American College of Cardiology: Cardiovascular Imaging, the research could lead to faster diagnosis and earlier detection of congestive heart failure—helping doctors treat patients more effectively and slow disease…
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Project MONAI is releasing MONAI v0.7, MONAI Label v0.2, MONAI Deploy v0.1, and announcing the MONAI Stream working group. The MONAI Deploy working group is excited to release the MONAI Deploy Application SDK v0.1, which helps bridge the gap from innovative research to clinical production. While MONAI Core focuses on training and creating models, MONAI Deploy focuses on defining the…
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NVIDIA data scientists this week took three of the top 10 spots in a brain tumor segmentation challenge validation phase at the prestigious MICCAI 2021 medical imaging conference. Now in its tenth year, the BraTS challenge tasked applicants with submitting state-of-the-art AI models for segmenting heterogeneous brain glioblastomas sub-regions in multi-parametric magnetic resonance imaging…
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Deep learning is essential for building AI models in medical imaging to help identify anomalies in images, generate automatic measurements, flag urgent cases, and longitudinal tracking. Explore the latest NVIDIA authored research presented during MICCAI 2021, the International Conference on Medical Image Computing and Computer Assisted Intervention. Accounting for Dependencies in Deep…
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Due to the success of the 2020 MONAI Virtual Bootcamp, MONAI is hosting another Bootcamp this year from September 22 to September 24, 2021—the week before MICCAI. The MONAI Bootcamp will be a three-day virtual event with presentations, hands-on labs, and a mini-challenge day. Applicants are encouraged but not required to have some basic knowledge in deep learning and Python programming.
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Using a newly developed AI algorithm, researchers from the University of Texas Southwestern Medical Center are making early detection of aggressive forms of skin cancer possible. The study, recently published in Cell Systems, creates a deep learning model capable of predicting if melanoma will aggressively spread, by examining cell features undetectable to the human eye. “We now have a…
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‘Meet the Researcher’ is a series spotlighting researchers in academia who use NVIDIA technologies to accelerate their work. This month’s spotlight features Peerapon Vateekul, assistant professor at the Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University (CU), Thailand. Vateekul drives collaboration activities between CU and the NVIDIA AI Technology Center…
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In this update, we look at the ways NVIDIA TensorRT and NVIDIA Triton Inference Server can help your business deploy high-performance models with resilience at scale. We start with an in-depth, step-by-step introduction to TensorRT and Triton. Next, we dig into exactly how Triton and Clara Deploy complement each other in your healthcare use cases. Finally, to round things out our whitepaper covers…
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A team of scientists from Argonne National Laboratory developed a new method for turning X-ray data into visible, 3D images with the help of AI. The study, published in Applied Physics Reviews, develops a computational framework capable of taking data from the lab’s Advanced Photon Source (APS) and creating 3D visualizations hundreds of times faster than traditional methods. “In order to make…
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Deep learning models have been successfully used in medical image analysis problems but they require a large, curated amount of labeled images to obtain good performance. Creating such annotations are tedious, time-consuming and typically require clinical expertise. To address this gap, Project MONAI has released MONAI Label v0.1 – an intelligent open source image labeling and learning tool…
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Five NVIDIA Inception partners were named finalists at the 2020-2021 Artificial Intelligence Tech Sprint, a competition aimed at improving healthcare for veterans using the latest AI technology. Hosted by the Department of Veterans Affairs (VA), the sprint is designed to foster collaboration with industry and academic partners on AI-enabled tools that leverage federal data to address a need for…
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The NVIDIA NGC team is hosting a webinar with live Q&A to dive into this Jupyter notebook available from the NGC catalog. Learn how to use these resources to kickstart your AI journey. Register now: NVIDIA NGC Jupyter Notebook Day: Medical Imaging Segmentation. Image segmentation partitions a digital image into multiple segments by changing the representation into something more meaningful…
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The NVIDIA NGC team is hosting a webinar with live Q&A to dive into our new Jupyter notebook available from the NGC catalog. Learn how to use these resources to kickstart your AI journey. NVIDIA NGC Jupyter Notebook Day: Building a 3D Medical Imaging Segmentation Model Thursday, July 22 at 9:00 AM PT Image segmentation deals with placing each pixel (or voxel in the case of 3D) of an…
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Breast cancer is the most frequently diagnosed cancer among women worldwide. It’s also the leading cause of cancer-related deaths. Identifying breast cancer at an early stage before metastasis enables more effective treatments and therefore significantly improves survival rates. Although mammography is the most widely used imaging technique for early detection of breast cancer…
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JPEG 2000 (.jp2, .jpg2, .j2k) is an image compression standard defined by the Joint Photographers Expert Group (JPEG) as the more flexible successor to the still popular JPEG standard. Part 1 of the JPEG 2000 standard, which forms the core coding system, was first approved in August 2002. To date, the standard has expanded to 17 parts, covering areas like Motion JPEG2000 (Part 3) which extends the…
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NVIDIA recently released Clara Train 4.0, an application framework for medical imaging that includes pre-trained models, AI-Assisted Annotation, AutoML, and Federated Learning. In this 4.0 release, there are three new features to help get you started training quicker. Clara Train has upgraded its underlying infrastructure from TensorFlow to MONAI. MONAI is an open-source…
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NVIDIA Inception partner DarwinAI developed a new AI model to detect COVID-19 in CT scans with 96% accuracy across a wide and diverse number of scenarios. The model, COVID-Net CT-2, was built using a number of large and diverse datasets created over several months with the University of Waterloo and is publicly available on GitHub. Last year, the startup launched an open source neural network…
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Getting AI up and running in hospitals has never been more important. Until recently, connecting an inference pipeline to perform analysis has had its challenges and limitations. There is a considerable amount of complexity in setting up and maintaining the hardware and software, deployment, configuration, and all workflow steps in an AI inference research pipeline. NVIDIA Clara Deploy…
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Building on the public alpha release announced at GTC 2020 in April, MONAI (Medical Open Network for AI) is an open source and community-supported PyTorch-based framework for healthcare imaging — providing domain-optimized foundational capabilities for developing deep learning training workflows. MONAI v0.2 brings new capabilities, examples, and research implementations for medical imaging…
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The Medical Open Network for AI (MONAI), is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized, foundational capabilities for developing a training workflow. Building upon the GTC 2020 alpha release announcement back in April, MONAI has now released version 0.2 with new capabilities, examples…
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Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the-art accuracy and can augment the medical imaging workflow with AI-powered insights. However, training robust AI models for medical imaging analysis is time-consuming and tedious and requires iterative experimentation with parameter…
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To help advance medical research while preserving data privacy and improving patient outcomes for brain tumor identification, NVIDIA researchers in collaboration with King’s College London researchers today announced the introduction of the first privacy-preserving federated learning system for medical image analysis. NVIDIA is working with King’s College London and French startup Owkin to enable…
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The medical imaging industry is undergoing a dramatic transformation driven by two technology trends. Artificial Intelligence and software-defined solutions are redefining the medical imaging workflow. Deep learning research in medical imaging is booming. However, most of this research today is performed in isolation and with limited datasets. This leads to overly simplified models which only…
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Deep Learning in medical imaging has shown great potential for disease detection, localization, and classification within radiology. Deep Learning holds the potential to create solutions that can detect conditions that might have been overlooked and can improve the efficiency and effectiveness of the radiology team. However, for this to happen data scientists and radiologists need to collaborate…
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The NVIDIA Transfer Learning Toolkit is now NVIDIA TAO Toolkit. The growing volume of clinical data in medical imaging slows down identification and analysis of specific features in an image. This reduces the annotation speed at which radiologists and imaging technicians capture, screen, and diagnose patient data. The demand for artificial intelligence in medical image analysis has…
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According to the American Cancer Society, over 200,000 people in the United States are diagnosed with lung cancer every year. To help with early detection, researchers at the New York University school developed a deep learning algorithm that can identify two common types of lung cancer with human accuracy. “The purpose of this study was to develop a deep-learning model for the automatic analysis…
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Artificial intelligence is revolutionizing how medical images are interpreted, helping medical professionals save time analyzing magnetic resonance imaging, CT scans, and X-rays. However, one of the significant challenges deep learning scientists working in the medical community face is the lack of accurate and reliable data to train their neural networks. For the first time…
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At GTC Japan in Tokyo, NVIDIA unveiled the Clara platform, a revolutionary computing architecture based on the NVIDIA Xavier AI computing module and NVIDIA Turing GPUs. The platform, which is a combination of hardware and software brings AI to the next generation of medical instruments. “The Clara platform addresses the great challenge of medical instruments: processing the massive sea of data…
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Fujifilm has become the first company in Japan to adopt the NVIDIA DGX-2 AI supercomputer for their AI development in healthcare, medical imaging, and its highly functional materials for displays. The announcement was made by NVIDIA Founder and CEO Jensen Huang at the company’s annual GTC Japan event in Tokyo. Akira Yoda, chief digital officer of Fujifilm, said, “Fujifilm applies AI in a wide…
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Stanford researchers developed a deep learning algorithm that can diagnose 14 types of heart rhythm abnormalities with cardiologist-level accuracy. They are able to identify irregular heartbeats, called arrhythmias, from sifting through hours of heart rhythm using electrocardiogram (ECG) signals generated by wearable monitors – which can be used to speed diagnosis and improve treatment for people…
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Researchers from Lehigh University in Pennsylvania developed a diagnostic technique that combines deep learning and cutting-edge imaging technology to detect in real-time the difference between cancerous and benign cells with over 90% accuracy. “The idea is that one day, if this technique could be used during surgery, it could complement the histopathology, potentially reducing the need for a…
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The Allen Institute for Cell Science launched a one-of-a-kind online portal of 3D cell images called Allen Cell Explorer that were produced using deep learning. The website combines large-scale 3D imaging data, the first application of deep learning to create predictive models of cell organization, and a growing suite of powerful tools. “This is the first time researchers have used deep learning…
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UK researchers developed a smartphone app using deep learning that lets people with Parkinson’s disease test their symptoms at home in just four minutes. “There’s very little understanding as to how Parkinson’s arises, and patients say that every day the condition is different,” says George Roussos at Birkbeck, University of London and co-author of the research paper. It is currently hard to…
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Google researchers developed a deep learning-based framework that automatically identifies tumors. “What we’ve trained is just a little sliver of software that helps with one part of a very complex series of tasks,” said Lily Peng, the project manager behind Google’s work. “There will hopefully be more and more of these tools that help doctors [who] have to go through an enormous amount of…
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Todd Raeker, Research Technology Consultant at the University of Michigan shares how a group of 50 researchers at University of Michigan are using GPUs and OpenACC to accelerate the codes for their data-driven physics simulations. The current versions of the codes use MPI and depend on finer and finer meshes for higher accuracy which are computationally demanding. To overcome the demands…
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Nicholas Bedworth, founder of SocialEyes, shares how they are developing cost-effective mobile AI diagnostic tools that can provide critically-needed medical services to low-resource societies. “Being able to diagnose people near to where they live – at home, at work, in the community, pharmacies – we can catch these diseases very early before they develop into extremely difficult to treat…
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Neurosurgeons and pathologists from University of Michigan Medicine developed a new imaging technique that can be used in the operating room to diagnose brain tumors more efficiently. Today’s workflow for determining a diagnosis during an operation requires the surgeon wait for 30 to 40 minutes while tissue is sent to a dedicated pathology lab for processing, sectioning, staining…
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Late last year, the NVIDIA Inception Program hosted a “Cool Demo Contest” for GPU-accelerated startups that are applying deep learning to their innovations. A variety of companies from around the world submitted their demos, ranging from defense to healthcare applications. Below are highlights from three of the 14 winners who each won a Pascal TITAN X GPU. Eating Smart Just Got Smart Now…
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Le Lu, staff scientist at the National Institutes of Health (NIH) shares how they are applying artificial intelligence techniques to assist cancer clinicians make better diagnostic decisions. Using NVIDIA Tesla GPUs and the cuDNN-accelerated Caffe deep learning framework, Lu and his team trained their model on nearly one million patient cases which helped them develop better medical image…
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Pattern recognition and classification in medical image analysis has been of interest to scientists for many years. Machine learning techniques have enabled researchers to develop and utilize complicated models to classify or predict various abnormalities or diseases. Recently, the successful applications of state-of-the-art deep learning architectures have rapidly expanded in medical imaging.
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Moises Hernandez Fernandez, PhD student at University of Oxford shares how GPUs are being used to accelerate the analysis of the human brain’s underlying anatomical and structural organization, which can lead to a better understanding of neurological disorders like Alzheimer’s or Multiple Sclerosis. Using Tesla K80 GPUs and CUDA, the group at Oxford Centre for Functional MRI of the Brain…
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Researchers from the National Institutes of Health in Bethesda, Maryland are using NVIDIA GPUs and deep learning to automatically annotate diseases from chest x-rays. Accelerated by Tesla GPUs, the team trained their convolutional neural networks on a publicly available radiology dataset of chest x-rays and reports to describe the characteristics of a disease, such as location…
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In an interview with Fortune, NVIDIA CEO Jen-Hsun Huang weighs in on data centers, driverless cars, and deep learning technology. Below are some excerpts from the interview: Fortune: What’s the current status of artificial intelligence? Jen-Hsun Huang: 2015 was a big year. Artificial intelligence is moving into the commercial world. AI has been worked on for many years, largely in research.
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The Facebook Artificial Intelligence Research (FAIR) lab announced a new Research Partnership Program to spur advances in Artificial Intelligence and machine learning — Facebook will be giving out 25 servers powered with GPUs, free of charge. The first recipient to receive 32 GPUs in four GPU servers is Klaus-Robert Müller of TU Berlin. “Dr. Müller will receive four GPU servers that will enable…
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Researchers at the Salk Institute for Biological Studies and several collaborators used NVIDIA GPUs to create a highly detailed 3D digital reconstruction of tissue from a rat’s hippocampus, the memory center of the brain. The reconstruction, powered by TITAN GPUs, helped the researchers precisely determine the number and size categories of synapses — the connections that carry signals between…
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For $200, AlemHealth has created Alembox which allows physicians to tap into a global network of radiologists and other specialists to get accurate diagnoses quickly. The box will allow physicians in developing countries high-quality, low-cost health IT services over a 3G mobile connection where the latest medical equipment, personnel and expertise may be scarce. Developed with the Jetson…
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The $150,000 award will go to a researcher or institution that has used NVIDIA technology to achieve breakthrough results with positive social and humanitarian impact. This includes – but is not limited to – the areas of disease research, drug design & development, medical imaging, energy & fuel efficiency, weather prediction, natural disaster response and cyber security. Our 2015 Global Impact…
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This week’s Spotlight is on Diego Rivera, a senior software engineer at Hologic, Inc. Hologic is a leading developer of medical imaging systems and surgical products, with an emphasis on serving the healthcare needs of women throughout the world. NVIDIA: Diego, tell us about your role at Hologic. Diego: I’m part of a team that has been able to deliver great solutions for breast cancer detection.
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