Machine Learning Model for Surgical Imaging Latency Reduction
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Solution Overview
Problem
Modern scientific or surgical imaging devices face high computational load and latency due to complex image processing workflows that require multiple sequential steps, which can be inefficient and delay real-time image processing.
Innovation Solution
Training a machine-learning model, such as a deep neural network, to perform image processing tasks in an integrated manner, replacing the entire image processing workflow and reducing computational complexity and latency by eliminating intermediate outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sequential image processing steps are performed, then comprehensive image processing tasks are achieved, but computational load and latency increase
Solution Approach 1:
The patent combines multiple sequential image processing steps into a single integrated machine learning model. The model performs multiple processing tasks (e.g., denoising, segmentation, feature extraction) simultaneously in one computational pass, eliminating the sequential execution of separate processing steps and thereby reducing latency while maintaining comprehensive processing capabilities.
Solution Approach 2:
The machine learning model is designed as a universal processor that can perform multiple image processing functions within a single model architecture. By training the model to handle diverse processing tasks concurrently, the system achieves multi-functionality without requiring separate specialized processing steps, thus reducing overall processing time and latency.
2Reliability
If multiple sequential image processing steps are performed, then comprehensive image processing tasks are achieved, but computational complexity increases
Solution Approach 1:
The patent merges multiple complex processing steps into a single machine learning model computation. Instead of executing several separate processing algorithms sequentially, the integrated model performs all necessary processing operations in one unified computational framework, thereby reducing the overall computational complexity while maintaining comprehensive processing functionality.
3Adaptability or versatility
If intermediate outputs are generated at each processing step, then processing flexibility is maintained, but processing efficiency decreases
Solution Approach 1:
The patent extracts and eliminates the intermediate output generation steps from the processing pipeline. By removing the creation of intermediate results at each processing stage, the system avoids unnecessary computational overhead and data handling, thereby improving processing efficiency while the final model output provides the necessary flexibility for various applications.
Data Source
AI summary
Examples relate to methods, systems, and computer systems for training a machine-learning model, for generating a training corpus, and for using a machine-learning model for use in a scientific or surgical imaging system, and to a scientific or surgical imaging system comprising such a system. A method for training a machine-learning model for use in a scientific or surgical imaging system comprises obtaining a plurality of images of a scientific or surgical imaging system, for use as training input images. The method comprises obtaining a plurality of training outputs that are based on the plurality of training input images and that are based on an image processing workflow of the scientific or surgical imaging system, the image processing workflow comprising a plurality of image processing steps. The method comprises training the machine-learning model using the plurality of training input images and the plurality of training outputs.