Cloud Digital Pathology Analysis System
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Solution Overview
Problem
Current digital pathology systems face challenges in performing interactive and scalable analysis of large histopathology images due to computational intensity and inadequate distributed computing capabilities, leading to slow analysis times and inefficiencies in image processing.
Innovation Solution
A multi-layer system is implemented, separating user interface and interpretation layers to distribute computationally intensive tasks to a back-end server, allowing for real-time analysis by dividing images into sub-sections for parallel processing and pre-processing images to reduce latency and improve responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If full analysis of large histopathology images is performed on existing desktop or web-browser systems, then analysis completeness is improved, but analysis speed deteriorates due to computational intensity and limited client-side resources
Solution Approach 1:
The system segments the analysis process into two distinct layers: a user interface layer for interaction and an interpretation layer for computationally intensive processing. This segmentation allows the heavy analytical work to be performed on the server side while maintaining responsive user interaction on client devices, thereby achieving both complete analysis and fast processing.
Solution Approach 2:
The patent introduces a back-end server as an intermediary between the client device and the analysis process. The server hosts the interpretation layer that performs full image analysis, acting as a mediator that handles computational tasks too intensive for client devices while enabling complete analysis without sacrificing speed.
2Measurement precision
If computationally intensive analytics are performed on entire histology images, then analysis accuracy is improved, but processing time deteriorates to the point where interactive execution becomes infeasible
Solution Approach 1:
The system performs preliminary actions by pre-processing images and preparing analysis frameworks in advance on the server side. This allows the computationally intensive work to be initiated and progressed before user interaction, reducing perceived processing time while maintaining accurate analysis results.
Solution Approach 2:
The architecture enables dynamic task distribution where the system can adjust between performing full analysis and providing preliminary results based on user needs and system state. This dynamic approach allows accurate analysis when needed while providing faster preliminary feedback to maintain interactivity.
3Adaptability or versatility
If large histology images are transferred between client and server for analysis, then analysis capability is improved, but communication overhead deteriorates and overwhelms distributed systems
Solution Approach 1:
The patent extracts the computationally intensive interpretation layer from the client device and relocates it to the server side. This extraction eliminates the need for continuous transfer of large image data between client and server, as the server processes images locally in the interpretation layer, thereby reducing communication overhead while maintaining full analysis capability.
4Power
If powerful distributed computing systems are used to handle digital pathology, then processing power is improved, but system complexity and resource requirements deteriorate making the system inadequate for pathology needs
Solution Approach 1:
The patent resolves the contradiction by transitioning from horizontal scaling (adding more powerful distributed nodes) to vertical scaling (consolidating processing power on the server side). This dimensional change allows the system to provide powerful processing capabilities through a simplified architecture where the server handles all intensive computations, reducing overall system complexity while maintaining high processing power.
Data Source
AI summary
A method and systems for cloud-based digital pathology include scanning received slides that include a pathology sample to produce a sample image in a shared memory, analyzing the sample image using one or more execution nodes, each including one or more processors, according to one or more analysis types to produce intermediate results, transmitting some or all of the sample image to a client device, further analyzing the sample image responsive to a request from the client device to produce a final analysis based on the intermediate results, and transmitting the final analysis to the client device.