CNN-RNN Anomaly Detection in Volumetric Medical Images
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Solution Overview
Problem
Current methods for anomaly detection in medical volumetric images, such as CT and MRI scans, rely heavily on human expertise and are inefficient due to high processing times, inaccuracy, and non-generalizability, leading to burdensome costs and potential delays in analysis results.
Innovation Solution
The use of neural network architectures combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs, specifically long short-term memory networks) for encoding and analyzing volumetric images, allowing for automated detection of anomalies by generating probability maps and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional radiologist review is used for anomaly detection in volumetric images, then high accuracy and reliability are achieved, but processing time increases and productivity decreases
Solution Approach 1:
A CNN-RNN based computer-aided detection system serves as an intermediary between the volumetric images and the radiologist. The system pre-analyzes images to generate anomaly probability maps and prioritize critical scans, allowing radiologists to focus their expert review on high-risk cases while maintaining high accuracy and improving overall processing throughput
Solution Approach 2:
The patent replaces the manual mechanical review process with an automated neural network system. The CNN-RNN architecture automatically detects patterns and anomalies in volumetric images, substituting the radiologist's manual inspection for initial screening while preserving human expertise for complex decision-making
2Extent of automation
If traditional CAD technologies are used for anomaly detection, then automation is increased, but measurement precision and reliability deteriorate due to high processing times and inaccuracy
Solution Approach 1:
The patent transforms the automated detection approach by changing key parameters: using deep learning architectures (CNN-RNN) instead of traditional handcrafted features, and generating probability maps instead of binary decisions. This enables the system to learn complex patterns automatically while maintaining high precision through probabilistic outputs that reflect uncertainty
Solution Approach 2:
The system combines multiple computational approaches into a composite solution: convolutional neural networks for feature extraction, recurrent neural networks for temporal modeling of image sequences, and probability map generation for uncertainty quantification. This composite architecture achieves both high automation and measurement precision
3Measurement precision
If exhaustive manual review of image slices is performed, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis by generating anomaly probability maps for all image slices before radiologist review. This pre-processing step identifies likely anomaly locations and prioritizes critical scans, allowing radiologists to conduct targeted reviews rather than exhaustive examination of all slices, thus maintaining precision while reducing time loss
Data Source
AI summary
Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.