Unified Neural Network for Abnormality Detection in Hospital Surveillance
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Solution Overview
Problem
Conventional surveillance systems in hospital environments are computationally resource intensive and inaccurate, often requiring separate neural networks for image processing and visualization, leading to increased system complexity and the likelihood of overlooking undesirable events.
Innovation Solution
A method and system using a single trained neural network to perform both image processing and visualization tasks, employing a variational autoencoder to generate localized two-dimensional or three-dimensional representations of abnormalities, which reduces computational resources and enhances accuracy in detecting and localizing anomalies within a defined area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural networks are used for image processing and visualization tasks, then the system can perform specialized functions, but the system complexity and computational resource requirements increase
Solution Approach 1:
The patent combines multiple neural network functions into a single unified neural network that performs both image processing and visualization tasks. This integration reduces the number of separate components while maintaining the specialized capabilities needed for accurate abnormality detection and localization in medical imaging environments.
Solution Approach 2:
The unified neural network is designed to perform multiple functions including image encoding, abnormality detection, and visualization generation within a single model architecture. This multi-functional approach eliminates the need for separate specialized networks while preserving the ability to handle different task requirements.
2Reliability
If separate neural networks are used for image processing and visualization tasks, then specialized functions can be performed, but the training data and time requirements increase
Solution Approach 1:
By merging the training processes of multiple separate neural networks into a single unified training framework, the patent reduces the total amount of training data needed and decreases training time. The unified network learns all required functions simultaneously through shared representations and joint optimization.
Solution Approach 2:
The unified neural network is trained in advance to perform all required functions including both processing and visualization tasks. This preliminary training establishes shared feature representations that can be reused across different tasks, eliminating the need for sequential training of separate networks.
3Device complexity
If conventional video surveillance is used, then the system is simple to implement, but the task becomes tedious and error-prone with undesirable events overlooked
Solution Approach 1:
The unified neural network automatically performs both image processing and visualization generation without requiring separate manual processing steps. The system self-services by integrating all detection and visualization functions within a single automated framework, eliminating manual intervention while maintaining simplicity.
Data Source
AI summary
Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.