Directed Machine Learning for Tissue Labeling Segmentation
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Solution Overview
Problem
Current machine learning approaches for tissue labeling in medical images require manual creation and combination of intensity and spatial models, lacking a quantitative method to determine optimal models, leading to potential over-fitting and reliance on large training datasets without evidence of model optimality.
Innovation Solution
A directed machine learning method that condenses intensity and location data into feature vectors, processed by multiple classifiers like Support Vector Machines (SVMs), allowing for automated model creation and combination of probabilities, transforming the image segmentation problem into a machine learning domain with quantitative evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual creation and combination of intensity and spatial models is used, then model interpretability is maintained, but device complexity and time consumption increase significantly
Solution Approach 1:
The system automatically creates and optimizes distribution models through self-service mechanisms. The machine learning algorithm autonomously learns optimal model parameters and combinations from training data, eliminating the need for manual model design and probability combination strategies. This self-service approach directly reduces both device complexity and time consumption in model creation.
2Productivity
If manual probability combination methods are used, then model interpretability is maintained, but productivity decreases due to tedious testing procedures
Solution Approach 1:
The patent replaces manual mechanical procedures of model testing and probability combination with automated machine learning systems. Instead of manually testing different distribution models and weightings, the system uses automated algorithms to evaluate multiple models and determine optimal combinations, significantly improving productivity while managing complexity through systematic automation.
3Adaptability or versatility
If histogram modeled distribution is used, then flexibility in modeling is improved, but over-fitting risk increases and large training data is required
Solution Approach 1:
The system dynamically changes model parameters and selection based on the specific characteristics of the input data. Instead of using fixed histogram-based distributions, the machine learning approach adapts distribution parameters and model types according to the learned patterns in training data, improving generalization while maintaining flexibility. This parameter adaptation reduces over-fitting by learning robust parameters that generalize across different datasets.
Data Source
AI summary
A method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image.