Ensemble ML Architecture for Liver Lesion Detection
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Solution Overview
Problem
Current methods for detecting liver lesions rely heavily on manual analysis by human experts, which is prone to errors and inefficient, especially with increasing image volumes, and existing automated systems lack accuracy in differentiating between false positives and negatives.
Innovation Solution
An ensemble machine learning model architecture comprising multiple trained decoders with counterbalanced loss functions is employed to process medical images, focusing on the liver region, which combines predictions to minimize erroneous detections and optimize specificity and sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by human experts is used for liver lesion detection, then diagnostic accuracy and pattern recognition capability are improved, but processing efficiency and scalability deteriorate due to increasing image volumes
Solution Approach 1:
The system segments the liver lesion detection task into multiple specialized decoder models, each trained with different loss functions to detect specific lesion characteristics. This segmentation allows parallel processing of multiple image batches simultaneously, improving throughput while maintaining diagnostic accuracy through specialized detection pathways.
Solution Approach 2:
The patent replaces the mechanical human expert analysis system with an automated ensemble machine learning system. The ensemble of decoders with counterbalanced loss functions automates the pattern recognition and lesion detection processes previously performed manually, achieving both high accuracy and scalable efficiency.
2Productivity
If existing automated detection systems are used, then processing efficiency is improved, but accuracy in differentiating false positives and negatives deteriorates
Solution Approach 1:
The patent merges multiple decoder models into an ensemble system, where each decoder is trained with a different loss function specialized for detecting specific types of errors (false positives vs. false negatives). The counterbalanced training approach combines these specialized detectors to achieve high accuracy in differentiating false positives and negatives while maintaining automated processing efficiency.
Solution Approach 2:
The system changes the training parameters (loss functions) of different decoders to specialize them for detecting different error types. By adjusting the loss function parameters during training, each decoder becomes optimized for specific detection tasks, and the ensemble combines these parameter-specialized models to achieve superior overall accuracy.
3Device complexity
If a single decoder model is used for lesion detection, then device complexity is reduced, but the ability to balance specificity and sensitivity deteriorates
Solution Approach 1:
The patent applies counterbalancing by training decoders with opposing loss function priorities - one decoder emphasizes sensitivity (detecting all potential lesions) while another emphasizes specificity (reducing false positives). These counterbalanced models work together in the ensemble to achieve optimal balance between sensitivity and specificity, with the counterweight approach preventing one bias from dominating.
Data Source
AI summary
A lesion detection ensemble machine learning model architecture comprising a plurality of trained machine learning (ML) computer models is provided. A first decoder of a lesion detection ML model processes a medical image input to generate a first lesion mapping prediction. A second decoder of the lesion detection ML model processes the medical image input to generate a second lesion mapping prediction. Combinational logic combines the first and second lesion mapping predictions to generate a combined prediction. Final lesion mapping output logic generates a final lesion prediction based on the combined lesion mapping prediction. The final lesion mapping output logic outputs the final lesion prediction for further downstream computing operations. The first decoder is trained with a first loss function that is configured to counterbalance a training of the second decoder that is trained using a second loss function different from the first loss function.