Diagnostic Lab Image Annotation Training Under Varied Imaging Conditions
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Solution Overview
Problem
Conventional diagnostic laboratory systems face high costs and time consumption in retraining machine vision systems for new sample containers due to the need for extensive manual annotation under ideal imaging conditions, which do not match actual deployment conditions.
Innovation Solution
A controllable imaging device within the laboratory system captures training images under varied conditions, allowing for automatic annotation and iterative training of AI models to adapt to different imaging scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed under ideal imaging conditions to train machine learning models, then annotation accuracy is improved, but training time and cost increase significantly
Solution Approach 1:
The system performs preliminary action by capturing images under multiple imaging conditions in advance and generating annotations for these pre-captured images. This allows the machine learning model to be trained on diverse conditions without requiring real-time manual annotation during deployment, reducing both training time and cost while maintaining annotation accuracy through pre-prepared diverse training data
Solution Approach 2:
The system creates copies of training data by capturing images under multiple different imaging conditions (different lighting, angles, positions). These copied images under varied conditions serve as additional training samples, allowing the model to learn robust features without requiring extensive manual annotation for each condition, thereby reducing overall annotation burden and training time
2Measurement precision
If manual annotation is performed under ideal imaging conditions to train machine learning models, then annotation quality is improved, but training cost increases excessively
Solution Approach 1:
The system changes imaging parameters (lighting conditions, camera angles, positions) to capture images under multiple conditions. By automatically capturing and annotating images across these parameter variations, the system reduces the need for expensive manual annotation work while maintaining high annotation quality through diverse training data that generalizes better to real-world conditions
Solution Approach 2:
The system creates multiple copies of training samples under different imaging conditions. These copied images serve as additional training data that reduce the need for extensive manual annotation, thereby lowering training costs while maintaining annotation quality through the diversity of captured conditions
3Adaptability or versatility
If machine learning models are trained to handle real-world imaging variations, then adaptability is improved, but the complexity of training data collection increases
Solution Approach 1:
The imaging device is designed with multi-functionality, capable of capturing images under multiple imaging conditions (different lighting, angles, positions) using the same device. This universal capability allows the system to collect diverse training data without requiring multiple specialized devices or complex data collection procedures, thereby improving adaptability while managing training data collection complexity
Solution Approach 2:
The system performs self-service by automatically capturing images under multiple imaging conditions and generating annotations without requiring external intervention for each condition. The imaging device and annotation system work together autonomously to create diverse training data, simplifying the data collection process while improving model adaptability to various real-world conditions
Data Source
AI summary
A method of updating training of an annotation generator of a diagnostic laboratory system includes providing an imaging device in the diagnostic laboratory system, wherein the imaging device is controllably movable within the diagnostic laboratory system; capturing a first image within the diagnostic laboratory system using the imaging device, the first image captured with at least one imaging condition; performing an annotation of the first image using the annotation generator to generate a first annotated image; and updating training of the annotation generator using the first annotated image. Other methods and systems are disclosed.