Ground-truth preparation in medical project
We developed a ground-truth preparation process for medical imaging projects where we use machine learning algorithms.
The model operation results are conditional on the quality of learning data and their relevant preparation.
The objective data prepared in the appropriate way which will be used to teach the model will guarantee the anticipated results. And excellent results are the success of the whole enterprise.
Preparing data for training medical algorithms – our offer
Pipeline
High degree of objectivity
Experienced radiologists
Model validation
We have organized the whole pipeline including both data annotation and verification of these annotations’ accuracy.
Our process allows us to ensure a high degree of objectivity in the analysis of a given study and to eliminate the risk related to an incorrect assessment resulting from personal experience of a given doctor.
We engage experienced radiologists with the expertise necessary to analyze a given study properly to prepare the data.
The test used for model validation and the test annotations are verified in three stages.
Preparing data for training medical algorithms – our offer
Pipeline
We have organized the whole pipeline including both data annotation and verification of these annotations’ accuracy.
High degree of objectivity
Our process allows us to ensure a high degree of objectivity in the analysis of a given study and to eliminate the risk related to an incorrect assessment resulting from personal experience of a given doctor.
Experienced radiologists
We engage experienced radiologists with the expertise necessary to analyze a given study properly to prepare the data.
Model validation
The test used for model validation and the test annotations are verified in three stages.
Why Graylight Imaging
We have organized the whole pipeline including both data annotation and verification of these annotations’ accuracy.
We engage experienced radiologists with the expertise necessary to analyze a given study properly to prepare the data.
Our process allows us to ensure a high degree of objectivity in the analysis of a given study and to eliminate the risk related to an incorrect assessment resulting from personal experience of a given doctor.
The test used for model validation and the test annotations are verified in three stages.
The pipeline of ground-truth preparation in medical project
Training data
We use data provided by the client or obtained by us, and we also leverage resources from proven, trusted vendors to develop advanced machine learning based models.
Team of experts
If necessary, we involve not only experienced radiologists but also other specialists. It is most important to ensure the maximum degree of objective analysis of a given study.
Prepare annotation
Our task is to ensure an efficient both flow of studies between doctors and to monitor the whole process. Obviously, the team of experts is in charge of preparing the annotations.
Evaluation
A second expert team reviews annotations, accepting or requesting improvements. Each study's annotation is double-checked during preparation and evaluation.
A test set chosing
After finishing the annotation process and receiving the expert final approval for all studies, a test set is then carefully selected from studies that were not used in training.
Validating
The studies' annotations from the test set are passed to a second radiologist from the evaluation team for assessment. Importantly, an expert has not previously assessed a study.
The process result
In sum, the test study and its annotations are evaluated three times. Finally, only after the study had been accepted by radiologists could it be used for model validation.
Why Graylight Imaging
We have organized the whole pipeline including both data annotation and verification of these annotations’ accuracy.
We engage experienced radiologists with the expertise necessary to analyze a given study properly to prepare the data.
Our process allows us to ensure a high degree of objectivity in the analysis of a given study and to eliminate the risk related to an incorrect assessment resulting from personal experience of a given doctor.
The test used for model validation and the test annotations are verified in three stages.
Learn more about our competences
Learn more about our competences
Let’s work on your challenges together!
Contact us:
Let’s work on your challenges together!
Contact us:
