Cell Annotation System with Adaptive Incremental Learning
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Solution Overview
Problem
Existing machine learning algorithms for cell image classification are limited by their inability to adapt to new classes and data over time, as they are trained with a static snapshot of data, making them unsuitable for scenarios with varying cell image appearances and unbalanced data sets.
Innovation Solution
A system that performs continuous learning and incremental prediction by using unsupervised learning to self-organize new cell images into class models, employing data divergence measures and similarity metrics to improve detection performance, allowing for automatic classification and annotation of cell images without requiring prior knowledge of cell types or modalities, and enabling the system to learn from incremental data arrival.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised machine-learning algorithms are used for cell image classification, then classification accuracy can be achieved with static training data, but the system cannot adapt to new classes or data arriving over time
Solution Approach 1:
The patent transforms the static classification system into a dynamic one by implementing incremental learning that continuously updates classification models as new cell image data arrives. The system dynamically adjusts to new classes and data distributions over time, resolving the contradiction between maintaining classification accuracy and adapting to new classes.
Solution Approach 2:
The system implements feedback mechanisms where classification results and new incoming data are continuously fed back into the learning process. This allows the model to learn from its predictions and continuously improve, maintaining accuracy while adapting to new classes through iterative refinement.
2Loss of time
If a fixed model is trained with a static snapshot of data, then training time can be controlled, but the model cannot handle unbalanced data sets or varying appearances of cell images
Solution Approach 1:
The patent implements continuous learning where the classification process never truly stops. Instead of periodic retraining on complete datasets, the system continuously processes incoming data in an incremental manner, maintaining adaptability to unbalanced data and varying appearances while avoiding the time cost of complete retraining cycles.
Solution Approach 2:
The system performs preliminary classification with available data and then incrementally improves the model as more data arrives. This allows the system to provide useful classification results immediately while continuing to adapt to unbalanced data distributions and varying cell appearances over time.
3Measurement precision
If manual labeling is used for cell image annotation, then accurate ground truth can be obtained, but the process is time-consuming and requires human expertise
Solution Approach 1:
The patent implements self-service annotation where the system automatically generates annotations through incremental learning from available data. The classification model serves itself by continuously improving through unsupervised and semi-supervised learning, reducing dependence on manual expert labeling while maintaining improving accuracy over time.
Solution Approach 2:
The system performs preliminary automatic annotation using the current model state, which provides immediate results without manual intervention. These preliminary annotations are then used to further train and improve the model incrementally, gradually approaching manual annotation quality while maintaining high productivity.
Data Source
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AI summary
A method, a computer readable medium, and a system for cell annotation are disclosed. The method includes receiving at least one new cell image for cell detection; extracting cell features from the at least one new cell image; comparing the extracted cell features to a matrix of cell features of each class to predict a closest class, wherein the matrix of cell features has been generated from at least initial training data comprising at least one cell image; detecting cell pixels from the extracted cell features of the at least one new cell image using the predicted closest class to generate a likelihood map; extracting individual cells from the at least one cell image by segmenting the individual cells from the likelihood map; performing a machine annotation on the extracted individual cells from the at least one new cell image to identify cells, non-cell pixels, and/or cell boundaries; calculating a confidence level for the machine annotation on the extracted individual cells from the at least one new cell image; and modifying the machine annotation if the confidence level is below a predetermined threshold.