Promaxo, an AI-powered medical imaging enhancement platform, announced the closing of an investment round of $4.17 million led by Huami. The medical technology company plans to use the new funds to accelerate its data strategy as it continues to incorporate artificial intelligence in imaging and image-based interventions. Using a combination of linear and genetic optimization algorithms, the company's medical imaging system forms the magnetic field within the field of view to maintain linearity and uniformity constraints while being thermally stable, the company said. "As an industry, we are just scratching the surface of how powerful MRI is poised to become in guided interventions, and we are proud to have Huami as a strategic investor as we introduce our truly open MRI system to the masses," said Dr. Amit Vohra, founder, and CEO of Promaxo. "Huami's mission is to connect health with technology, and we see tremendous opportunity in imaging to expand our growth opportunities. Companies such as Promaxo are disrupting the locations, applications, and costs of medical imaging. Huami has resources, such as miniaturization engineering expertise, that can help accelerate Promaxo's scaling, growth, and success, and we look forward to what our partnership can develop," said Huami Chief Operating Officer Mike Yeung.
Nvidia Chief Executive Jen-Hsun Huang, intoduces new graphics processing products and advances based ... [ ] on their Kepler GPU computing architecture technology. Huang demonstrated how GPU's operating in cloud servers can now be used to work, play games or render video, during his keynote at the GPU Technology Conference in San Jose, California. "Within 20 years, machines will be capable of doing anything man can do." Take a stab at when this quote is from. You've surely heard about Artificial Intelligence (AI) before. "AI" often conjures up images of intelligent robots taking over the world.
Artificial intelligence is a vital component in the fight against COVID-19. Healthcare benefits greatly from machine learning and artificial intelligence techniques that allow for better and faster mapping of the virus as well as for more comprehensive research to administrate the right treatment and create a vaccine. The National Institutes of Health has launched the Medical Imagining and Data Resource Center (MIDRC) to deliver AI-based solutions for the new type of problems the world is facing in the actual climate. The goal is to combine the power of AI and medical imaging to better understand and retaliate against COVID-19. Moreover, their goal is to be able to use medical imaging to create personalized treatments for patients with COVID-19.
A connection between the General Linear Model (GLM) in combination with classical statistical inference and the machine learning (MLE)-based inference is described in this paper. Firstly, the estimation of the GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix, that is, in terms of the inverse problem of regressing the observations. In other words, both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value at the least-squares solution. Subsequently, from this relationship we derive a statistical test based on a more refined predictive algorithm, i.e. the (non)linear Support Vector Machine (SVM) that maximizes the class margin of separation, within a permutation analysis. The MLE-based inference employs a residual score and includes the upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how the parameter estimations derived from each model resulted in different classification performances in the equivalent inverse problem. Moreover, using real data the aforementioned predictive algorithms within permutation tests, including such model-free estimators, are able to provide a good trade-off between type I error and statistical power.
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To determine the efficacy of deep learning in assessing endotracheal tube (ETT) position on radiographs. Images were split into training (80%, 18368 images), validation (10%, 2296 images), and'internal test' (10%, 2296 images), derived from the same institution as the training data.
It is no secret that mammography services faced a significant set-back this year when the COVID-19 pandemic erupted. Eventually, the service line was able to rebound from the catastrophic 92-percent plummet it experienced during the summer months – but, that was not all that happened. That recovery was, without a doubt, a success, but it was by no means the only positive development with mammography during 2020. This was the year to watch advances in artificial intelligence tools in breast imaging. To pinpoint the ones that will be most impactful, Diagnostic Imaging spoke with Randy Miles, M.D., MPH, assistant professor of radiology at Harvard Medical School.
Although there are many good reasons to remember year 2020, but we have to accept the sad reality i.e. COVID-19 as well which is the major downside of same year. The best parts are the automation and the enhancement of customer experience through Companies are getting advanced in many ways to provide tailored and elegant solutions. Technologies like 5G (6G and Quantum computing are little far for now), cutting-edge medical diagnostics systems, real-time -powered e-commerce, consumer electronics, and smart personal assistants are few examples. 's bundle of joy (Machine learning and its advancement to and neural network) as horizontal scaling techniques have taken almost every other businesses and technology to the next level.
Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. In this work, we have used short-time Fourier transform (STFT) based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets. The first study achieves an accuracy, sensitivity, specificity, precision and F1 score of 95.4%, 96.3%, 92.4%, 97.6% and 96.98% respectively while the second study shows accuracy, sensitivity, specificity, precision and F1 score of 94.2%, 95.5%, 90.3%, 96.8% and 96.1% respectively. Finally, the third study shows a precision of 98.29% on the noisy PASCAL dataset with transfer learning approach. All the three proposed approaches outperform most of the recent competing studies by achieving comparatively high classification accuracy and precision, which make them suitable for screening CVDs using PCG signals.
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and multi-parties data training and analyses are infeasible due to the privacy and data security concerns. In this paper, we propose an extendable and elastic learning framework to preserve privacy and security while enabling collaborative learning with efficient communication. The proposed framework is named distributed Asynchronized Discriminator Generative Adversarial Networks (AsynDGAN), which consists of a centralized generator and multiple distributed discriminators. The advantages of our proposed framework are five-fold: 1) the central generator could learn the real data distribution from multiple datasets implicitly without sharing the image data; 2) the framework is applicable for single-modality or multi-modality data; 3) the learned generator can be used to synthesize samples for down-stream learning tasks to achieve close-to-real performance as using actual samples collected from multiple data centers; 4) the synthetic samples can also be used to augment data or complete missing modalities for one single data center; 5) the learning process is more efficient and requires lower bandwidth than other distributed deep learning methods.
Medical Imaging has been used in several applications in the healthcare industry. Deep Learning solutions have exceeded many healthcare tasks in detecting and diagnosing abnormalities in medical data. In January 2020, we noticed Google's DeepMind AI outperformed radiologists in detecting breast cancer, according to Nature's publication. Data management is one of the most critical steps in deep learning solutions. The size of healthcare data is reaching 2314 Exabytes of new data by 2020.