Machine Learning Classification for Digital Microscopy Images
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Solution Overview
Problem
Current methods for identifying particles, such as those in medical or ecological samples, rely heavily on human visual classification, which can be time-consuming and prone to errors, especially when distinguishing between similar particles that are indistinguishable visually, and often struggle with capturing accurate data at varying focal depths due to limited depth of field in microscopy.
Innovation Solution
The development of systems and methods utilizing digital imaging and machine learning algorithms, specifically deep neural networks, for automated classification of particles, incorporating multiple focal depths and out-of-channel data sources like DNA tests to establish ground truth, allowing for more accurate classification and training of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human visual classification is used to identify particles, then the method is simple and requires no complex equipment, but it is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the mechanical human visual classification system with an automated machine learning-based image analysis system. The system uses trained neural networks to automatically classify particles, eliminating the need for manual visual inspection while improving both accuracy and efficiency. This substitution directly addresses the contradiction by removing the time-consuming human element while maintaining or enhancing classification reliability.
Solution Approach 2:
The patent implements preliminary training of machine learning models using labeled particle images before actual classification. This pre-training phase creates a knowledge base that enables rapid, accurate classification during operation. By performing the learning action beforehand, the system achieves both high accuracy and fast processing speed during actual use, resolving the time-accuracy tradeoff.
2Measurement precision
If single focal plane imaging is used, then the imaging process is simple and fast, but particles at different depths cannot be distinguished
Solution Approach 1:
The patent transitions from two-dimensional single-plane imaging to three-dimensional multi-plane imaging by capturing images at multiple focal depths. This dimensional extension allows particles at different depths within the sample to be visualized and classified separately, significantly improving measurement precision without requiring complex hardware modifications beyond standard microscopy capabilities.
Solution Approach 2:
The patent segments the imaging process into multiple focal plane captures, with each plane being imaged and processed separately. This segmentation allows the system to handle depth information systematically by treating each focal plane as an independent dataset that can be individually analyzed and then integrated, managing complexity through structured division of the imaging task.
3Measurement precision
If multiple focal depths are captured for each region, then classification accuracy improves, but the amount of data to be processed increases
Solution Approach 1:
The patent extracts and processes only the relevant features and information from the multi-focal-plane images using machine learning algorithms. Rather than processing all raw image data equally, the system identifies and extracts key discriminative features that are most important for accurate classification, reducing the effective data volume that needs intensive processing while maintaining high accuracy.
Solution Approach 2:
The patent uses the multi-focal-plane images as training data to create a trained machine learning model that can then rapidly classify new particles. The system creates a computational copy or representation of the knowledge learned from the extensive training data, allowing fast inference on new data without reprocessing the original large datasets. This copying approach enables accurate classification while reducing processing time and computational load on new data.
Data Source
AI summary
Systems, methods, and devices for training models or algorithms for classifying or detecting particles or materials in microscopy images are disclosed. A method includes receiving a plurality of microscopy images of a specimen and a classification for the specimen. The plurality of microscopy images includes a first image captured at a first magnification and a second image captured at the first magnification with a different focus than the first image. The method includes training a machine learning model or algorithm using the plurality of images, wherein the first image and the second image are provided with one or more labels indicating the classification.