Augmented Medical Image Blending for Model Generalization
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Solution Overview
Problem
Machine learning algorithms used in healthcare to identify diseased or injured tissue from medical images often suffer from over-fitting, leading to incorrect classifications and failure to generalize well.
Innovation Solution
The technique involves creating an augmented image by overlaying an image of healthy tissue onto an image of unhealthy tissue, and vice versa, to create a hybrid image that can be used as training data for machine learning models, improving their accuracy in distinguishing between healthy and unhealthy tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on standard medical images, then the training process is simple and fast, but the models suffer from over-fitting and fail to generalize well to new data
Solution Approach 1:
The patent applies preliminary action by pre-processing medical images through blending healthy and unhealthy tissue regions before training the model. This preparation creates augmented training data that inherently contains varied tissue compositions, enabling the model to learn more robust features and improve generalization ability before deployment.
Solution Approach 2:
The patent changes the parameters of training images by blending different tissue types (healthy and unhealthy) in various proportions and configurations. This parameter transformation creates diverse training samples that prevent over-fitting and enhance the model's ability to generalize to new medical images.
2Measurement precision
If machine learning models learn patterns from training data, then they can identify diseased tissue, but they may learn overly specific patterns that lead to false positives or false negatives
Solution Approach 1:
The patent applies local quality by selectively blending healthy and unhealthy tissue regions within images, creating training samples with localized variations in tissue composition. This approach allows the model to learn both specific disease patterns and normal variations, improving detection precision while maintaining classification reliability through balanced representation.
Data Source
AI summary
A method for improving machine learning algorithm performance is described. The method may comprise receiving a first constituent image of human tissue; receiving a second constituent image of human tissue; overlapping a portion of the second constituent image on a portion of the first constituent image to create an augmented image; and training a model using a dataset comprising at least the augmented image.