Edge AI Endoscopy Diagnosis With Lightweight CNN Compression
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Solution Overview
Problem
Existing AI-based decision-support systems for GI endoscopy face challenges in computation resource limitations when implemented on edge computers, necessitating a system that can perform lesion detection and quality control efficiently with limited resources while ensuring real-time analysis.
Innovation Solution
A decision-support system utilizing a plurality of CNNs with learnable kernels modeled as linear combinations of fixed kernels, combined with knowledge distillation and multi-task model sharing, to reduce computation requirements and enable edge computing for GI endoscopy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based server is used for AI-based decision-support system, then computation resources are sufficient, but communication delay and network reliability become problems
Solution Approach 1:
The patent introduces an edge computing intermediary between the endoscope and cloud server. The edge computer performs preliminary AI-based analysis locally to reduce communication delay, while maintaining cloud server connection for comprehensive processing. This intermediary approach resolves the contradiction by providing local real-time processing capability without completely abandoning cloud-based computational power.
Solution Approach 2:
The system segments the computation task into two parts: local edge computing for immediate response and cloud-based processing for comprehensive analysis. By dividing the workload, the system achieves both low latency local decisions and sufficient computational power from the cloud, resolving the contradiction between computation capacity and communication delay.
2Loss of time
If edge computer is used for AI-based decision-support system, then communication delay is reduced, but computation resources become limited
Solution Approach 1:
The system dynamically adjusts the computation workload between edge device and cloud server based on task complexity and available resources. Simple tasks are processed locally at the edge device for minimal delay, while complex tasks are offloaded to the cloud server. This dynamic workload distribution resolves the contradiction by adapting the system architecture to match computational needs.
Solution Approach 2:
The patent merges edge computing and cloud computing into a hybrid system. The edge computer handles immediate processing needs while the cloud server provides comprehensive computational power. This combination allows the system to achieve both low communication delay for local decisions and sufficient computation resources from the cloud, resolving the contradiction between these two requirements.
3Measurement precision
If complex machine-learning models are employed for lesion detection, then detection performance improves, but computation resource requirements increase exponentially
Solution Approach 1:
The system segments the AI model execution between edge device and cloud server. The edge device runs lightweight models for immediate lesion detection, while the cloud server processes complex models for comprehensive analysis. This segmentation allows high detection performance through complex models while distributing the computational burden to resolve the contradiction between performance and resource requirements.
Solution Approach 2:
The system changes the model complexity parameter dynamically based on available computation resources and task requirements. When edge device resources are sufficient, simpler models are used for fast processing. When cloud resources are accessed, more complex models with higher detection performance can be deployed. This parameter adaptation resolves the contradiction between detection performance and computation resource consumption.
4Productivity
If multiple AI tasks are executed concurrently for comprehensive analysis, then detection completeness improves, but system complexity increases
Solution Approach 1:
The system segments concurrent AI tasks across multiple devices - the edge device handles immediate analysis tasks while the cloud server processes comprehensive analysis tasks. This spatial segmentation of tasks across devices allows comprehensive detection completeness while managing system complexity through distributed architecture rather than local monolithic complexity.
Solution Approach 2:
The edge computer acts as an intermediary that coordinates between different AI tasks and the cloud server. It manages task Queues, prioritizes processing, and coordinates resource allocation. This intermediary coordination layer simplifies the overall system complexity while enabling comprehensive concurrent analysis through proper task management and resource orchestration.
Data Source
AI summary
In a decision-support system for gastrointestinal (GI) endoscopy, convolutional neural networks (CNNs) are set up to perform decision-support tasks according to endoscopic images. Each learnable kernel used in the CNNs is advantageously modeled as a linear combination of a set of fixed kernels for simplifying kernel learning, giving a lightweight kernel model to advantageously reduce required computation resources. Further computation-resource reduction can be made by CNN model compression via knowledge distillation and by using multi-task CNNs. It enables the decision-support system to be realized as an edge computing system near a site of performing endoscopic examinations. The system can be automatically configured for esophagogastroduodenoscopy (EGD) or colonoscopy. In the system, lesion-detection results and quality-control results can be seamlessly integrated to provide value-added results, which are more valuable to the endoscopist than separately considering the lesion-detection results and quality-control results.