Siamese Network Anomaly Detection for Medical Images
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Solution Overview
Problem
Existing detection and classification networks, particularly in medical imaging, face challenges such as complex training processes, systematic errors, and lack of reliability due to noisy or false training data, leading to mispredictions with high confidence.
Innovation Solution
A method and system that combines a generative network or autoencoder with a Siamese network to detect anomalies in images. The system is trained using normal and abnormal image datasets, generating confidence scores based on similarity measures to improve reliability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning neural networks are used for image detection and classification, then the system can process images and generate outputs, but the reliability of the output is compromised due to noisy or false training data leading to mispredictions with high confidence
Solution Approach 1:
The patent introduces an intermediary verification mechanism that acts as a mediator between the deep learning network output and the final decision. This intermediary layer validates predictions by checking multiple criteria including confidence thresholds, anomaly consistency, and cross-validation with training data distributions, thereby filtering out unreliable high-confidence mispredictions caused by noisy training data
Solution Approach 2:
The system implements feedback loops where prediction results are continuously evaluated against ground truth data and training distributions. The feedback mechanism adjusts confidence scoring and triggers re-evaluation or human review when anomalies are detected in the prediction reliability, creating a self-correcting system that improves output reliability over time
2Adaptability or versatility
If training data includes noisy or false images, then the system can learn from diverse data, but systematic errors occur leading to mispredictions with high confidence
Solution Approach 1:
The patent applies preliminary actions by performing data quality assessment and anomaly detection during the training data preparation phase. The system pre-identifies and flags noisy or false images using statistical analysis and anomaly detection algorithms before they can corrupt the training process, thereby maintaining detection accuracy while still utilizing diverse training data
Solution Approach 2:
The system dynamically adjusts training parameters such as learning rates, confidence thresholds, and data weighting based on the quality metrics of training images. By changing these parameters adaptively, the system can learn from diverse data while compensating for the presence of noisy samples, preventing systematic errors
3Reliability
If medical images are annotated with ground truth data, then the training can be performed, but the process requires great effort and cost and can be subjective due to uncertainty in the images
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically generates pseudo-ground-truth labels and performs self-verification of annotations. The deep learning model generates initial annotations that are then validated against multiple criteria including consistency checks, cross-model agreement, and uncertainty quantification, reducing the need for manual expert annotation while maintaining high reliability
Solution Approach 2:
The system applies partial annotation strategies where only the most uncertain or critical regions of medical images require expert annotation. By using confidence scoring and uncertainty estimation, the system identifies and prioritizes only the necessary portions for manual review, significantly reducing annotation time and cost while maintaining training data quality
4Reliability
If the system uses confidence values to indicate reliability, then the output reliability can be measured, but the complexity of the system increases
Solution Approach 1:
The patent segments the confidence measurement function into distinct modular components including confidence scoring modules, uncertainty estimation modules, and reliability validation modules. Each segment handles a specific aspect of reliability measurement independently, making the overall system more manageable and maintainable despite the increased complexity
Data Source
AI summary
Anomalies in images are detected. A generative network and/or an autoencoder (“G/A-Network”), a Siamese network, a first training-dataset of normal images and a second training-dataset of abnormal images are provided. The G/A-network is trained to produce latent data from input images and output images from the latent data, wherein the training is performed with images of the first training-dataset, wherein a loss function is used for training at least at the beginning of training, and the loss function enhances the similarity of the input images and respective output images. The Siamese network is trained to generate similarity measures between input images and respective output images, wherein the training is performed with images of the first training-dataset and the second training-dataset in that images of both training-datasets are used as input images for the G/A-network and output images of the G/A-network are compared with their respective input images by the Siamese network.