Data Labeling: How Data Annotation Service is Transforming the Medical Sector?
COVID-19 has undoubtedly accelerated the application of AI in the healthcare industry, such as virus surveillance, diagnosis, and patient risk assessments. AI-powered robots and digital assistants with real-time monitoring and analysis have enabled doctors to provide more effective and personalized treatment.
Garbage in, Garbage out
Data Quality
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. It gives algorithms the ability to “learn” from training data so as to identify patterns and make decisions with little human intervention.
Many enterprises intend to apply AI to their business practices. They have a glut of data, such as vast amounts of images and documents. However, as the saying goes, “garbage in, garbage out,” getting high-quality labeled data is not easy work. The challenge is how to process and label the data in order to make it useful and productive. Many organizations are struggling to get AI and ML projects realizable due to data labeling limitations.
Deficiency
According to a report “the Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, hospitals produce 50 petabytes of data per year. Almost 90% are digital medical images i.e. scans like MRIs or CTs. However, more than 97% of data go unanalyzed or unused.
Unstructured data needs to be labeled correctly before it is fed into an algorithm to train an ML model. As DJ Patil and Hilary Mason write in Data-Driven, “cleaning and labeling the data is often the most taxing part of data science and is frequently 80% of the work.”
A Human-Powered Data Labeling Platform with Real-time Monitoring and High Efficiency
Designed to empower AI and ML industry, ByteBridge.io promises to usher in a new era for data labeling and accelerates the advent of the smart AI future.
ByteBridge is a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service. Aiming at increasing quality, efficiency, and flexibility for the data labeling industry, it specializes in high volumes, high variance, complex data, and provides a full-stack solution for AI companies.
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Accuracy and Efficiency
- Dealing with complex tasks, the task is automatically transformed into tiny component to maximize the quality level as well as maintain consistency.
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All work results are completely screened and inspected by the machine and human workforce.
- In fact, ByteBridge owns millions of registered workers with 2D boxing daily outputs to 100,000.
Flexibility
Individually decide when to start your projects and get your results back instantly.
- Clients can set labeling rules directly on the dashboard.
- Clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation
- Progress preview: clients can monitor the labeling progress in real-time on the dashboard
- Result preview: clients can get the results in real-time on the dashboard
- Real-time Outputs: clients can get real-time output results through API. We support JSON, XML, CSV, etc., and we can provide customizable datatype to meet your needs
End
Thanks to data annotation service, human beings benefit from the implementation of AI systems into the medical industry, from diagnosis to treatment, from drug experiments to generalization.
“It is dedicated to providing the best data solutions for AI development and unleashing the real power of data. “We focus on addressing practical issues in different application scenarios for AI development through one-stop data service. ” said Brian Cheong, CEO and founder ByteBridge.
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How Data Training Accelerates the Implementation of AI into Medical Industry was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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