The Healthcare industry is under enormous pressure, especially in the midst of the Covid-19 period. The unexpected global pandemic has presented overwhelming challenges to human beings. Scientists, medical experts, doctors, and nurses across the globe have undertaken their responsibility to fight against the disease. However, with a shortage of healthcare labor force, it is undeniable that we are short in medical capacities.
There’s no doubt that Covid-19 has been a catalyst for strengthening the increasing connection and cooperation between AI and the healthcare industry.
AI and ML can be powerful methods in healthcare: medicine research, diagnosis, disease prevention, and control, patient treatment, even administrative and personnel management. AI/ML-enabled systems improve their capabilities and effectiveness by automating the most repetitive and homogenous activities. It is currently moving out of the labs and getting into the real-world health sector.
When it comes to medical CT scans, ML’s applications can cover the entire cycle, from image creation, reconstruction to diagnosis, outcome prediction. AI-assisted Machines use computer vision to detect patterns that the human eye can hardly recognize, and correlate them with similar medical image data so as to identify possible diseases and prepare analysis reports. In this way, X-ray can compute tomography (CT) scan, magnetic resonance imaging (MRI), and other image-based test reports are easily screened to predict various illnesses with accuracy and efficiency.
Some healthcare companies are now using ML technology to detect organ anomalies, such as identifying tumors from an MRI scan of the brain. Along with millions of labeled medical images, the affected area is detected by ML algorithms. For example:
AI semantic segmentation can be used in liver and brain diagnosis;
Polygon annotation can be used in dentistry;
Bounding box in kidney stone;
Medical image annotations provide results with greater accuracy in early detection. Medical imaging diagnosis is seen as a powerful method for future applications in the health sector.
High-quality training data is the key to building ML models and helps to improve medical image-based diagnosis. However, a great challenge in this field is the lack of high-quality training data. Specifically, medical imaging annotations have to be performed by clinical specialists, which is costly and time-consuming.
As DJ Patil and Hilary Mason write in Data-Driven, “Cleaning the data is often the most taxing part of data science and is frequently 80% of the work.” The lack of high-quality datasets presents an overwhelming challenge for the machine learning industry, limiting their ability to provide the “right data” to answer specific questions. Currently, most medical research organizations have limited access to data samples from a certain geographic area.
The hardest part of building AI products is not the AI or algorithms but data preparation and labeling. For example, retinal images are used to develop automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration. In order to achieve the goal, millions of medical images need to be labeled under various conditions. This is laborious as it requires the identification of very small structures and usually takes hours for experts to annotate them with accuracy.
Imagine one day, patients can simply go through a fast AI scan as diagnosis; smart wearable devices, such as Apple Watch, can analyze physical data, note abnormality and generate an alarm before you are about to have a heart attack or stroke; medical detection and prediction can be fully automated and supervised with little human intervention. Such scenes can definitely be realized in the coming future, thanks to ML and AI technology.
Machine Learning has achieved unprecedented success in computer vision and other industries so far. And now it is drastically revolutionizing the healthcare area with indispensable support from automated data labeling service.
Aware of those challenges, ByteBridge moves a big step forward through its automated data collection and labeling platform. It allows researchers to have access to high-quality labeled datasets related to health care and public health.
ByteBridge is a human-powered and ML-powered data collection and labeling SAAS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- 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 results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
In addition, we can provide personalized annotation tools and services according to customer requirements.
- We comply with principles and rules in each region and we respect data the way your company does.
- The CEO of the company supervises data management as a DPO (Data Protection Officer)
- According to the guideline, if there is data leakage, we will inform the customer within 72 hours
- GDPR personal privacy and data protection regulations compliance
- Workers location, process, and authority restriction
- No original data leak as the data is compressed and preprocessed
- Support private cloud and privatization deployment
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: email@example.com
1 How Data Training Accelerates AI into Medical Industry?
2 How Medical-AI Could Play a Bigger Role with the Aid of Data Labeling Service?
3 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
4 Eight Common Data Annotation and Labeling Tools
5 The Best Data Labeling Company in 2021