Deep Learning Model Loading for Medical Image Processing
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Solution Overview
Problem
Medical image processing systems face inefficiencies due to the time-consuming process of loading complex deep learning models into memory, especially when multiple models with different frameworks and programming languages are used, leading to increased overhead and resource competition.
Innovation Solution
Loading multiple models into memory at application startup and maintaining them for reuse, with separate server processes created for each programming language and framework, allowing for efficient processing of multiple images by allocating CPU and GPU resources optimally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If models are loaded into memory at application startup, then processing time is reduced and models can be reused, but memory usage increases
Solution Approach 1:
The patent applies preliminary action by loading all required deep learning models into memory at application startup before any image processing tasks begin. This ensures that models are pre-loaded and ready for immediate use, eliminating the need to load models during actual processing operations. The system identifies all models needed for potential processing tasks and loads them into server memory in advance, so when images need processing, the models are already available in memory rather than being loaded from disk at that moment.
2Adaptability or versatility
If multiple models with different frameworks and programming languages are used, then processing capability is enhanced, but system complexity increases and models are repeatedly switched out and reloaded
Solution Approach 1:
The patent applies segmentation by creating separate server processes for models with different programming language requirements. Instead of trying to manage all models in a single process, the system divides models into groups based on their programming language needs and creates dedicated server processes for each group. This segmentation allows each server process to manage its specific models efficiently without interference from other programming languages, reducing the complexity of model switching and reloading while maintaining the ability to use multiple frameworks and languages.
3Productivity
If models are loaded at startup, then processing speed increases, but more memory is required to retain all models
Solution Approach 1:
The patent applies universality by creating server processes that can handle multiple types of processing tasks using different models. Each server process is designed to be multi-functional, capable of executing various deep learning models for different processing tasks. This allows the system to maintain multiple models in memory within each server process and efficiently switch between them based on processing needs, maximizing the utility of the loaded models and justifying the memory investment by enabling a single server process to perform multiple different processing functions.
Data Source
AI summary
Methods and systems for processing medical images. One method includes, in response to startup of an application using an algorithm, creating a server process supporting a programming language associated with the algorithm and loading a plurality of deep learning models used by the algorithm into a memory of the server process to create in-memory models. The method also includes processing a first set of one or more medical images with the server process using the algorithm and at least one model selected from the in-memory models, maintaining the in-memory models in the memory of the server process after processing the first set of one or more medical images, and, in response to a request to process a second set of one or more medical images, processing the second set of one or more medical images using the algorithm and at least one of the in-memory models.