Unsupervised Medical Data Analysis via Adversarial Multi-Encoder
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Solution Overview
Problem
Current supervised learning methods for medical data analysis require labeled data, which is time-consuming and costly to obtain, especially in the medical domain where expert labeling is necessary. Additionally, these methods may not accurately predict newly distributed data.
Innovation Solution
An unsupervised learning-based medical data analysis apparatus and method using a machine learning model trained with a multi-encoder, including a first encoder for rapid anomaly detection and a second encoder for extracting features described as normal, based on an adversarial generative neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used to train machine learning models for medical data analysis, then the model can predict specific diseases with high accuracy on training data, but it requires time-consuming and costly expert labeling of data
Solution Approach 1:
Instead of training the model to recognize abnormal conditions using labeled data (supervised approach), the patent inverts the approach by training the model to generate normal images from normal data (unsupervised approach). The model learns the distribution of normal medical images and can then identify abnormalities by detecting deviations from this learned distribution, eliminating the need for expert labeling while maintaining diagnostic accuracy.
Solution Approach 2:
The system performs self-training by automatically learning the characteristics of normal medical data without requiring external expert annotations. The unsupervised learning algorithm autonomously extracts features and patterns from unlabeled data, enabling the model to detect abnormalities independently without time-consuming human intervention for data preparation.
2Measurement precision
If supervised learning is used to train machine learning models, then the model can accurately predict training data distribution, but it may not predict reliable information regarding newly distributed data
Solution Approach 1:
The patent changes the fundamental parameter of training approach from supervised learning (using labeled data with ground truth) to unsupervised learning (using unlabeled data). This parameter change enables the model to learn the inherent structure and distribution of normal medical data without being constrained by training data labels, thereby improving its ability to generalize to new data distributions while maintaining accuracy on known conditions.
3Loss of time
If unsupervised learning is used to train machine learning models, then the time and cost for data labeling is reduced, but the model may require complex multi-encoder architectures with multiple encoders for effective anomaly detection
Solution Approach 1:
The patent segments the anomaly detection task into multiple functional components within the multi-encoder architecture. Each encoder is specialized for specific functions: some encoders extract features from input images, while others generate normal images or compute similarity metrics. This segmentation allows the complex unsupervised learning system to be broken down into manageable, specialized modules that work together to achieve effective anomaly detection without requiring a single monolithic complex model.
Data Source
AI summary
The present disclosure relates to an apparatus and a method for analyzing medical data based on unsupervised learning. By using a machine learning model based on an adversarial generative neural network to detect and notify anomalies in medical data, the present disclosure allows accurate and rapid reading, in addition to saving time and cost incurred by reading.