Imaging Controller Self-Learning for Microscopy Settings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In microscopy, finding an optimal operational setting for image quality is cumbersome and time-consuming, requiring users to adjust multiple parameters for each image acquisition.
Innovation Solution
An imaging system comprising an imaging device and a controller that uses a machine learning algorithm to predict and provide a preferred operational setting, which can be adjusted by the user before image acquisition. The controller updates the setting based on user feedback, allowing for efficient learning and adaptation during actual use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually adjust multiple setting parameters to achieve optimal image quality, then image quality can be optimized, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs self-learning by automatically analyzing user feedback (discard information) about acquired images and autonomously updating the preferred operational setting using a machine learning algorithm, eliminating the need for manual parameter adjustment while maintaining optimal image quality
Solution Approach 2:
The system implements a feedback mechanism where user decisions to discard or accept images are captured and fed back to the machine learning algorithm, which continuously refines the preferred operational setting based on this feedback loop
2Extent of automation
If a machine learning algorithm is used to learn rules for setting parameter adjustment during manufacturing, then automated setting provision is achieved, but the system requires a separate learning process before actual image acquisition
Solution Approach 1:
The system transitions from a static pre-trained machine learning model to a dynamic system that continuously adapts during actual use by incorporating real-time user feedback, allowing the preferred operational setting to evolve and improve based on specific user preferences and imaging conditions
Solution Approach 2:
The learning process is integrated into the continuous workflow of image acquisition, allowing the system to learn and adapt continuously during actual use rather than requiring a separate preliminary learning phase, thus maintaining productive action throughout
3Measurement precision
If users must manually vote on image quality in a separate learning process, then the system can learn from user preferences, but the process adds additional time and effort before image acquisition
Solution Approach 1:
The system merges the learning process with the image acquisition workflow by automatically capturing user feedback (discard decisions) during normal operation, eliminating the need for a separate voting process and integrating preference detection into the continuous imaging workflow
Data Source
Figure 1
Figure 2
AI summary
Disclosed is a controller (104) for providing an operational setting of an imaging device (102). The controller (104) is configured to provide a user with a preferred operational setting for acquiring an image. The controller (104) is further configured to receive a user input and to generate a discard information based on the user input, said discard information indicating whether or not the image generated by the imaging device (102) is discarded by the user. The controller (104) is further configured to update the preferred operational setting using a machine learning algorithm based on the discard information.