Labeled data

Computer Vision Startup Encord Launches DICOM Image Annotation Tool To Eradicate Manual Data Labelling for Healthcare Industry

Retrieved on: 
Friday, April 22, 2022
Technology, Medical Devices, Software, Biotechnology, Radiology, Health, General Health, Data Management, Oncology, Deep learning, CEO, Twitter, Memorial Sloan Kettering Cancer Center, CRV, MRI, Entrepreneurship, Automation, Machine learning, Labeled data, King's College London, Efficiency, Y Combinator, DICOM, CT, Method, 3D, AI, Doctor Foster, FDA, Medical imaging, Medical device

Encord , the platform for data centric computer vision, has released the first purpose-built 3D annotation tool for healthcare AI that enables users to train and run models to automate medical-imaging annotation in 3D for modalities including CT, X-ray, and MRI.

Key Points: 
  • Encord , the platform for data centric computer vision, has released the first purpose-built 3D annotation tool for healthcare AI that enables users to train and run models to automate medical-imaging annotation in 3D for modalities including CT, X-ray, and MRI.
  • Harnessing the power of automation through deep learning, the DICOM annotation tool replaces manual processes that make AI development expensive, time-consuming and difficult to scale.
  • Not only can Encords DICOM tool save time and money but it provides a data pipeline built around the images.
  • The DICOM annotation tool will slot into Encords existing platform, taking full advantage of the rest of its data pipeline features.

Alegion Teams with Malaysian Government Agency to Expand Machine Learning Workforce Opportunities

Retrieved on: 
Tuesday, May 25, 2021
Machine learning, Cybernetics, Learning, Prime Minister's Department, Labeled data, Articles, Malaysian Muslims

AUSTIN, Texas, May 25, 2021 /PRNewswire-PRWeb/ --Alegion Inc., a leading training data platform provider for machine learning, and Malaysian government agency Yayasan Peneraju Pendidikan Bumiputera (Yayasan Peneraju) announce their agreement for Alegion to provide training and certification in machine learning data labeling, addressing the rapidly evolving technologies of artificial intelligence (AI) and machine learning (ML).

Key Points: 
  • AUSTIN, Texas, May 25, 2021 /PRNewswire-PRWeb/ --Alegion Inc., a leading training data platform provider for machine learning, and Malaysian government agency Yayasan Peneraju Pendidikan Bumiputera (Yayasan Peneraju) announce their agreement for Alegion to provide training and certification in machine learning data labeling, addressing the rapidly evolving technologies of artificial intelligence (AI) and machine learning (ML).
  • Alegion, based in Austin, TX, provides a complete training data solution for enterprise-grade machine learning.
  • Alegion's training data platform enables efficient and accurate annotation of video, images, and text to support advanced machine learning and artificial intelligence initiatives.
  • Yayasan Peneraju Pendidikan Bumiputera is an agency under the Malaysian Prime Minister's Department focused on strengthening capacity building towards sustainable Bumiputera talent.

Synthetaic Awarded AFWERX Contract to Leverage Synthetic Data and AI-Powered Object Detection

Retrieved on: 
Thursday, May 20, 2021
Software, Technology, Data management, Satellite, Machine learning, Computer data, Labeled data, Data set

Synthetaic empowers ad hoc labeling and searching through a new approach of massive data scale processing and AI model building.

Key Points: 
  • Synthetaic empowers ad hoc labeling and searching through a new approach of massive data scale processing and AI model building.
  • \xe2\x80\x9cPreviously, we had to label geo-spatial data by hand, which required weeks or months, if not even years, of work.
  • With RAIC, non-expert analysts can now build a robust AI for object detection, search, data labeling, and categorization across massive datasets, and in under an hour.
  • AFWERX will help us showcase the dual commercial and government potential of Synthetaic in geospatial, data collection, and labeling markets.\xe2\x80\x9d\nSynthetiac has raised $4.5M funding to date.

New Deep Learning Discovery Paves Way for AI Interpretation of Brainwave Data

Retrieved on: 
Monday, January 25, 2021
Branches of biology, Articles, Academic disciplines, Emerging technologies, Cybernetics, Learning, Machine learning, Electroencephalography, Labeled data, Supervised learning, Artificial neural network

Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.

Key Points: 
  • Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.
  • This means there are typically not enough labelled examples for supervised deep neural networks to learn from in order to create an efficient AI.
  • While labelled-EEGs identifying sleep stages and brain activity are scarce, there is ample unlabeled data that exists.
  • Banville found that when limited numbers of labelled data were available, his self-supervised learning approach outperformed traditional supervised learning methods that rely purely on labelled data.

New Deep Learning Discovery Paves Way for AI Interpretation of Brainwave Data

Retrieved on: 
Monday, January 25, 2021
Branches of biology, Articles, Academic disciplines, Emerging technologies, Cybernetics, Learning, Machine learning, Electroencephalography, Labeled data, Supervised learning, Artificial neural network

Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.

Key Points: 
  • Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.
  • This means there are typically not enough labelled examples for supervised deep neural networks to learn from in order to create an efficient AI.
  • While labelled-EEGs identifying sleep stages and brain activity are scarce, there is ample unlabeled data that exists.
  • Banville found that when limited numbers of labelled data were available, his self-supervised learning approach outperformed traditional supervised learning methods that rely purely on labelled data.

New Deep Learning Discovery Paves Way for AI Interpretation of Brainwave Data

Retrieved on: 
Thursday, January 21, 2021
Science, Research, Mobile, Wireless, Technology, Software, Branches of biology, Articles, Academic disciplines, Emerging technologies, Neurotechnology, Cybernetics, Learning, Machine learning, Electroencephalography, Labeled data, Supervised learning, Artificial neural network

Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.

Key Points: 
  • Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate.
  • This means there are typically not enough labelled examples for supervised deep neural networks to learn from in order to create an efficient AI.
  • While labelled-EEGs identifying sleep stages and brain activity are scarce, there is ample unlabeled data that exists.
  • Banville found that when limited numbers of labelled data were available, his self-supervised learning approach outperformed traditional supervised learning methods that rely purely on labelled data.

Sixgill Announces HyperLabel, The Fastest Path To Implementing Machine Learning

Retrieved on: 
Tuesday, July 30, 2019
Data management, Technology, Engineering, VoIP, Manufacturing, Mobile, Wireless, Venture capital, Security, Professional services, Audio, Video, Telecommunications, Software, Networks, Internet, Electronic design automation, Articles, Machine learning, Academic disciplines, Cybernetics, Cognition, Emerging technologies, Learning, Training, validation, and test sets, Labeled data, Artificial intelligence, Labeling theory

HyperLabela new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLCoffers the fastest path to creating high-quality labeled datasets for better ML models.

Key Points: 
  • HyperLabela new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLCoffers the fastest path to creating high-quality labeled datasets for better ML models.
  • This will allow developers, engineers and data scientists to spend less time labeling and more time training their ML models.
  • Because HyperLabel is so easy to use with the power of ML, labeling projects will be more error free.
  • HyperLabel, by Sixgill, is a complete application for creating, automating, updating, and managing annotated datasets for Machine Learning.

Sixgill Announces HyperLabel, The Fastest Path To Implementing Machine Learning

Retrieved on: 
Tuesday, July 30, 2019
Machine learning, Cognition, Learning, Academic disciplines, Training, validation, and test sets, Labeled data, ML, Labeling theory

SANTA MONICA, Calif., July 30, 2019 /PRNewswire-PRWeb/ -- HyperLabela new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLCoffers the fastest path to creating high-quality labeled datasets for better ML models.

Key Points: 
  • SANTA MONICA, Calif., July 30, 2019 /PRNewswire-PRWeb/ -- HyperLabela new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLCoffers the fastest path to creating high-quality labeled datasets for better ML models.
  • This will allow developers, engineers and data scientists to spend less time labeling and more time training their ML models.
  • Because HyperLabel is so easy to use with the power of ML, labeling projects will be more error free.
  • HyperLabel, by Sixgill, is a complete application for creating, automating, updating, and managing annotated datasets for Machine Learning.