Neural Network Classifier for Digital Microscopy Cytology
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Solution Overview
Problem
Current methods for identifying and classifying particles in cytology images rely heavily on human visual detection, which can be time-consuming and prone to errors, and struggle to distinguish between indistinguishable particles, limiting the accuracy and efficiency of particle analysis.
Innovation Solution
The development of systems and methods utilizing digital imaging and machine learning algorithms, specifically deep neural networks, for automated classification and detection of particles, incorporating out-of-channel data for ground truth training to enhance classification accuracy beyond human capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If human visual detection is used for particle identification, then the method is simple to implement, but the accuracy is limited and time-consuming
Solution Approach 1:
The patent replaces the mechanical human visual detection system with an automated machine learning classification system. The machine learning model processes digital microscopy images to identify and classify particles, substituting human expertise with algorithmic analysis that can handle large volumes of data consistently and accurately without manual intervention.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the digital microscopy images and the final particle classification. The machine learning model acts as a mediator that learns from training data and applies learned patterns to classify particles, bridging the gap between raw image data and meaningful diagnostic information.
2Measurement precision
If human experts analyze entire samples, then comprehensive coverage is achieved, but the process is time-consuming
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes and classifies particles in digital microscopy images without requiring continuous human expert intervention. The system serves itself by autonomously analyzing entire samples, making decisions, and generating classifications, thereby eliminating the time constraint associated with manual expert analysis while maintaining comprehensive coverage.
3Productivity
If machine learning algorithms are used, then accuracy and speed are improved, but system complexity increases
Solution Approach 1:
The patent employs a universal machine learning classification system that can handle multiple particle types and classification categories through a single model architecture. The machine learning model is designed to be multi-functional, capable of identifying various particles including red blood cells, white blood cells, platelets, and abnormal cells, thereby managing complexity through a unified approach rather than requiring separate systems for each particle type.
4Measurement precision
If machine learning models are trained with out-of-channel data, then classification accuracy beyond human capabilities is achieved, but data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance using out-of-channel data, such as molecular test results, genetic information, or other complementary data sources. This pre-training process enables the model to learn complex patterns and relationships before actual particle classification, allowing it to achieve superior accuracy without requiring extensive data processing during the actual analysis phase.
Data Source
AI summary
The disclosure relates to machine learning classification of cells/particles in microscopy images. A method includes inputting an image having invisible features into an initial neural network classifier (INNC) of a convolutional neural network. The INNC is trained using images with ground truth derived from out-of-channel mechanisms. The method includes generating an intermediate classification from the original image. The intermediate classification and the original image are input into a final neural network classifier (FNNC) that comprises one or more bypass layers to feed forward an initial, final classification from a final activation layer to a final convolutional layer thereby bypassing a final pooling layer. The final convolutional layer has an increased kernel size and more filters than the initial convolutional layer. The final classification is generated based on the invisible features in the original image and outputted.