« We have successfully partnered with Ingedata on multiple projects covering hundreds of CT scans. By setting up a team of radiologists with project managers in the long run, we have achieved very high quality and swift execution of the projects. This setup builds trust and alleviates a lot of the burden during training and ramp up, with constant feedback helping to improve the annotation set up. We have obtained consistent and clean results both in complex protocols and precise segmentations. »
BENJAMIN RENOUST DATA AND KNOWLEDGE COORDINATION MANAGER @MEDIAN TECHNOLOGIESThe healthcare industry is rapidly adopting artificial intelligence to facilitate patient care and medical treatment, improve infrastructure, and achieve the highest possible quality of care.
This technology automates a few tasks and guides health professionals in using data-driven information. Its many applications make it a particularly useful technology in the healthcare industry, especially as the sector faces the challenges of aging populations, limited resources and increasing demand at lower costs.
Voxel-precise segmentation of regions of interest in MRI and CT scans, by radiologists.
These annotations are used to train computer vision models at generating 3D models of organs.
Identification and classification of cells using landmark positioning or polygons.
These annotations are generated by cytotechnicians and anatomopathologists to train computer vision models at biomarker detection.
Reading of medical imagery and medical reports to locate regions of interests in DICOM files.
These annotations are generated to train a computer vision model at detecting lesions from MRI and CT scans.
Voxel-precise segmentation of regions of interest in MRI and CT scans, by radiologists.
These annotations are used to train computer vision models at generating 3D models of organs.
Identification and classification of cells using landmark positioning or polygons.
These annotations are generated by cytotechnicians and anatomopathologists to train computer vision models at biomarker detection.
Reading of medical imagery and medical reports to locate regions of interests in DICOM files.
These annotations are generated to train a computer vision model at detecting lesions from MRI and CT scans.
We offer high-end services with our unique data production methodology and, more importantly, our wonderful team of 500. As a people company, Ingedata thrives on bringing talents from developing countries on the international AI scene.
Ingedata offers an extensive portfolio of Computer Vision and NLP models catering for the Healthcare industry.
We work on Medical and Healthcare projects in various domains. We have a strong experience in annotating medical images such as lung CT-scans or head X-rays and are currently working on the training of Computer Vision models to detect objects of interest in these types of images.
Ingedata’s quality process guarantees that models are high quality and fully compliant with all security regulations.
All processing is done in compliance with GDPR requirements, which is important for such sensitive data.
Learn more about our approach and explore our healthcare use cases
With more than 100 projects in the field of ML model training, we are recognized for our remarkable know-how in production management and our guarantee of quality.
Learn MoreExternalizing your data can represent a significant risk in terms of loss, deterioration, or theft. At Ingedata, your projects are designed and carried out in-house, from our secure production centers.
Ingedata's annotators have degrees ranging from bachelor's to engineer's or doctorate in your field. All our teams work from our production centers and are trained in the specific requirements of preparing data for machine learning.
Accelerate the optimization of your algorithm by using data prepared specifically for you. We collect, enrich, and categorize your data to build your own datasets.
We fit seamlessly into your current production mode, take over any coordination and adapt our team to your specific constraints in terms of data volume and quality.
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(Bôndy - 2024)