Medical Image Quality Factor for Training Data Similarity
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Solution Overview
Problem
Existing machine learning systems for medical imaging in radiation therapy lack a reliable method to assess the similarity between new data points and training data, leading to unreliable predictions and inefficient resource utilization.
Innovation Solution
A quality factor is generated to measure the similarity between new data samples and training data by computing distances and applying a model to determine the confidence level of applying the machine learning technique, ensuring the data sample is within the training data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning systems are used for medical imaging in radiation therapy, then prediction speed and efficiency are improved, but reliability of predictions deteriorates due to lack of data similarity assessment
Solution Approach 1:
The patent implements a feedback mechanism by computing a quality factor that measures the similarity between new data samples and training data distribution. This quality factor feeds back into the machine learning prediction process, allowing the system to assess whether predictions should be trusted. When the quality factor indicates low similarity, the system can flag or reject predictions, thereby improving reliability while maintaining the speed benefits of ML processing.
Solution Approach 2:
The patent introduces an intermediary quality factor computation step between data input and final prediction output. This intermediary layer computes distances between new data samples and training data distribution, then uses these distances to generate a quality factor that mediates the prediction process. This intermediary mechanism enables reliable filtering of low-quality predictions without slowing down the overall ML processing pipeline.
2Quantity of substance
If computational resources are allocated to process all data samples through machine learning, then prediction completeness is improved, but resource efficiency deteriorates due to processing unsuitable data
Solution Approach 1:
The patent applies preliminary action by computing the quality factor before executing the full machine learning prediction process. By assessing data similarity in advance using distance computations and quality factor generation, the system can pre-filter out unsuitable data samples that would waste computational resources. This preliminary quality assessment prevents unnecessary processing of data samples that fall outside the training data distribution, thereby improving resource efficiency while maintaining complete processing of valid samples.
3Adaptability or versatility
If the machine learning model processes data outside training distribution, then adaptability is improved, but manufacturing precision deteriorates due to erroneous results
Solution Approach 1:
The patent converts the potential harm of processing out-of-distribution data into a benefit by using the quality factor to identify and flag such cases. Rather than blindly processing all data samples and risking inaccurate predictions, the system leverages the quality factor computation to detect when data samples fall outside the training distribution. This allows the system to adaptively handle diverse data while maintaining precision by identifying cases where predictions should be treated with caution or rejected.
Data Source
AI summary
Systems and methods are disclosed for performing operations comprising: receiving a plurality of training medical images used to train a machine learning technique; applying a model to the plurality of training medical images to generate a distribution representation of the plurality of training medical images; computing a first distance between a given training medical image in the plurality of training medical images and the distribution representation; computing a second distance between a new medical image and the distribution representation; and computing a quality factor indicating a measure of similarity between a new medical image and the plurality of medical images as a function of the first distance and the second distance.