The Role and Contribution of Data Labeling in Controlling the COVID-19

The Role and Contribution of Data Labeling in Controlling the COVID-19

Artificial Intelligence and Machine Learning is getting integrated into more complex fields, thanks to training data now available for various types of models with better accuracy at higher quantity. And thanks to data labeling services, the machine learning training data is available as per the customize needs and algorithm compatibility.

Congruently, AI in healthcare is becoming more vital due to unexpected disease arise infecting mass population disturbing the economy growth. Coronavirus or COVID-19 is the newest pandemic need to control using the cutting-edge technology in healthcare sector.

AI in Healthcare

Big data in healthcare is playing a decisive role in gathering the information of various patients and making them available for machine learning. Computer vision based visual perception AI model need the labeled data, so that machine can learn from various patterns and detect such diseases in real-life use.

Scientist, medical experts, doctors and nurses across the globe have undertaken their responsibility to fight against the disease. And machine learning engineers are trying to develop the AI model that can detect such diseases without human intervention.

Training Data for Machine Learning

And to train these AI models, huge amount of training data is created by the data labeling companiesIn case of medical images, the diseases or infected area is precisely annotated to make recognizable to machines. Cogito is one the well-known data annotation companies providing the data labeling service for healthcare sector helping ML engineers to make AI not only possible but also more effective.

AI-enabled machines use the computer vision to detect patterns that human eye can hardly catch and correlate them with similar medical image data to identify possible diseases and prepare reports after analysis. X-ray, CT scan, MRI and other image-based test reports can be easily screened to predict various illness in an automated, accurate, and fast way.

Many healthcare companies are nowadays using ML technology identify the organ abnormalities, like detecting the tumors through MRI or CT scan, through millions of labeled image datasets to show the affected area and train the algorithms for detecting such diseases.

Medical Image Annotation

Different types of medical image annotation techniques are used to annotate the body organs or other parts of body. To diagnosis the liver and brain semantic segmentation image annotation technique is used. While on the other hand polygon annotation can be used in dentistry; bounding box in kidney stone; annotation detection in cancer cells, and etc.

Also Read: Role of Medical Image Annotation in the AI Medical Image Diagnostics for Healthcare

Medical image annotations provide results of greater accuracy in the early detection, diagnostics and treatment of disease as well as understanding the normal. Hence, annotating these images precisely is more important to ensure the accuracy and help the AI model learn properly and make right diagnosis in various scenarios helping doctors to provide the timely and right treatments at less cost.

Limitations of Medical Image Labeling

To develop a full functional AI model for medical imaging analysis you need high-quality training data. And lack of high quality data is a great challenge in this field. And in case of medical imaging annotations you need radiologists or medical experts to perform have to be performed the data annotation task which is costly and time-consuming process.

Actually, owing to lack of high quality data and annotation presents an overwhelming challenge for machine learning industry, limiting the ability to provide the “right data” that can help the model in answering the specific questions. Currently, most medical research organizations have limited access to data samples from a certain geographic areas.

The most crucial part of developing an AI model is not the AI or algorithms but preparation and labeling of data. For an example, retinal images are used to develop automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration.

And in order to do that datasets of millions of medical images need to be labeled by various conditions structurally. This is laborious process that requires identifying even very small structures and takes long hours for medical experts to annotate the images carefully.

Overcoming the Data Labeling Challenges

Apart from that there are many challenges while labeling the data, especially data generated for computer vision based AI model for healthcare sector. However, top data annotation companies are using the advance and resourceful data labeling process to produce the high-quality training data for different types of AI model for the different fields.

To ensure the accuracy and efficiency, these companies are now adopting the combination of human-powered automated data annotation techniques. AI-assisted data labeling is providing the new efficiency in producing the high-quality data at better speed with high accuracy.

However, there are data labeling challenges faced by the data annotation companiesand they are trying to overcome the problems to ensure supply the enormous amount of datasets for wide-ranging AI models. And right now these companies are also playing a big role in providing the datasets to control the COVID-19 with annotated medical images helping machines learn in detecting such diseases.

Cogito is one the best data annotation companies providing the medical image annotation services to annotate the medical images for dentistry and other sub-fields. Working with team of doctors, radiologist and medical experts are working here to generate the high-quality training data for machine learning and deep learning algorithms making available at lowest cost with data security and privacy.

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