Autofluorescence Microscopy and ML for Abnormal Cell Detection
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Solution Overview
Problem
The interpretation of tissue samples for cancer detection requires substantial training and experience, and existing methods are not efficient in rapidly and accurately identifying abnormal cells.
Innovation Solution
The use of autofluorescence microscopy in conjunction with machine learning models to detect abnormal cells by analyzing both stained and unstained tissue samples, where the first ML model identifies candidate abnormal cells in stained images and the second ML model confirms these cells using autofluorescence images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional pathologist examination methods are used, then diagnostic accuracy can be maintained through expert judgment, but the process is time-consuming and lacks efficiency in rapidly identifying abnormal cells
Solution Approach 1:
The patent replaces the mechanical manual examination process by pathologists with an automated machine learning-based detection system. The system uses trained ML models to analyze tissue images and automatically identify abnormal cells, eliminating the need for human pathologists to manually examine each cell while maintaining high diagnostic accuracy through computational analysis
Solution Approach 2:
The patent creates digital copies of tissue samples through imaging techniques and analyzes these copies using machine learning models. Instead of physically examining tissue under a microscope, the system creates and processes digital representations of the tissue, allowing rapid analysis of multiple samples simultaneously while maintaining diagnostic accuracy
2Productivity
If machine learning models are used to detect abnormal cells, then the speed of detection is improved, but the complexity of the system increases
Solution Approach 1:
The patent divides the detection system into separate functional modules: image acquisition, first ML model processing (candidate identification), second ML model processing (confirmation), and result output. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high efficiency through automated multi-stage processing
Solution Approach 2:
The patent introduces trained machine learning models as intermediary components between the tissue sample and the final diagnosis. These models act as mediators that process image data and provide abnormal cell identifications, simplifying the interaction between the complex imaging system and the diagnostic output while maintaining high detection efficiency
3Device complexity
If a single ML model is used to identify abnormal cells, then the system is simpler, but false positives and false negatives increase
Solution Approach 1:
The patent employs a two-stage ML model approach where the first model performs preliminary identification of candidate abnormal cells, and the second model performs confirmation. This preliminary action by the first model allows the system to filter and prioritize potential abnormalities before final verification, reducing false positives and negatives while maintaining reasonable system complexity
Solution Approach 2:
The patent implements a feedback mechanism where the output of the first ML model (candidate abnormal cells) serves as input to the second ML model for confirmation. This feedback loop allows the system to continuously verify and refine its identifications, improving reliability by cross-validating results between two models while managing complexity through structured multi-stage processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and accurate identification of abnormal cells, improving the efficiency and accuracy of cancer detection by reducing false positives and negatives.
Implementation Method 1
receiving an autofluorescence image of the unstained tissue sample
Data Source
AI summary
One example method includes receiving an image of a tissue sample stained with a stain; determining, by a first trained machine learning (“ML”) model using the image, a first set of abnormal cells in the tissue sample; receiving an autofluorescence image of the unstained tissue sample; determining, by a second trained ML model using the autofluorescence image and the first set of cells, a second set of abnormal cells, the second set of abnormal cells being a subset of the first set of abnormal cells; and identifying the abnormal cells of the second set of abnormal cells.