18470171. BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS simplified abstract (Siemens Healthcare GmbH)
Contents
- 1 BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS
Organization Name
Inventor(s)
Dominik Neumann of Erlangen (DE)
BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18470171 titled 'BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS
Simplified Explanation
The abstract describes a method where a machine-learning model is pre-trained in an unsupervised manner using data from contrast-enhanced medical imaging to predict time-related information. This pre-trained model is then used to build another machine-learning model to predict semantic context information for images from the same imaging measurement.
- Machine-learning model pre-trained in an unsupervised manner to predict time-related information from contrast-enhanced medical imaging data.
- Use of pre-trained model to build another model for predicting semantic context information for images from the same imaging measurement.
Potential Applications
This technology could be applied in the field of medical imaging analysis, specifically in improving the interpretation and understanding of contrast-enhanced images.
Problems Solved
This technology helps in predicting both time-related and semantic context information from medical imaging data, which can aid in diagnosis and treatment planning.
Benefits
The technology can potentially improve the accuracy and efficiency of analyzing contrast-enhanced medical images, leading to better patient outcomes.
Potential Commercial Applications
- Enhancing medical imaging software for improved analysis and interpretation of contrast-enhanced images.
Possible Prior Art
There may be existing methods or systems that utilize machine learning for medical imaging analysis, but specific prior art related to pre-training models in an unsupervised manner for predicting time-related and semantic context information from contrast-enhanced images may not be readily available.
Unanswered Questions
How does this technology compare to existing methods for medical imaging analysis?
This article does not provide a direct comparison to existing methods or technologies in the field of medical imaging analysis.
What are the limitations or challenges of implementing this technology in real-world medical settings?
The article does not address potential limitations or challenges that may arise when implementing this technology in actual medical practice.
Original Abstract Submitted
In a computer-implemented method, a machine-learning model is pre-trained in an unsupervised manner to predict time-related information based on data obtained from a contrast-enhanced medical imaging measurement. This pre-trained machine-learning model is then used to build another machine-learning model to predict semantic context information for images determined from the contrast-enhanced medical imaging measurement.
