Disease Classification via Multi-Resolution Cell Segmentation
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Solution Overview
Problem
Current techniques face challenges in accurately classifying cell types from tissue samples using microscopic images, particularly due to the complexity of distinguishing between normal and cancerous cells, which hampers precise disease diagnosis.
Innovation Solution
A computer-implemented method and system that processes microscopic images by removing backgrounds, segmenting single-cell images, applying filters to decrease resolution, assigning labels, and training machine learning models to predict cell classifications, utilizing convolutional neural networks for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing microscopic image analysis techniques are used, then the analysis process is simple, but the classification accuracy of cell types is insufficient
Solution Approach 1:
The patent segments the complex classification task into multiple processing stages: background removal, image segmentation to isolate individual cells, feature extraction at multiple resolutions, and hierarchical classification. This segmentation allows the system to achieve high classification accuracy by systematically processing different aspects of the images through specialized computational modules rather than attempting single-step classification.
Solution Approach 2:
The patent introduces multi-resolution analysis as an additional dimension to the classification process. By extracting features at multiple resolution levels and combining them hierarchically, the system captures both global tissue context and fine cellular details, thereby improving classification accuracy without requiring a single overly complex model.
2Loss of information
If high resolution is maintained throughout processing, then image detail is preserved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial processing at different resolution levels. Instead of processing all images at full resolution throughout, it performs initial processing at lower resolutions to capture gross features, then applies higher resolution analysis only where needed for detailed cellular classification. This selective multi-resolution approach preserves necessary image details while significantly reducing overall computational time.
Solution Approach 2:
The patent performs preliminary background removal and low-resolution feature extraction before conducting detailed high-resolution analysis. This preliminary processing at reduced complexity prepares the data for subsequent fine-grained classification, ensuring that detailed information is preserved where critical while avoiding unnecessary high-resolution processing throughout the entire pipeline.
3Measurement precision
If machine learning models are trained without multi-resolution filtering, then training is faster, but classification accuracy decreases due to local optima entrapment
Solution Approach 1:
The patent segments the feature extraction process into multiple resolution levels, with each level contributing specific features to the training data. This multi-resolution feature segmentation provides the machine learning model with hierarchical information that helps avoid local optima by presenting diverse feature representations at different scales, thereby improving classification accuracy without excessively prolonging training time.
Solution Approach 2:
The patent changes the resolution parameter across different processing stages and feature extraction levels. By systematically varying resolution parameters and combining features from multiple levels, the training process benefits from diverse parameter representations that help the model escape local optima and achieve better generalization, balancing training efficiency with improved accuracy.
Data Source
AI summary
Provided is a method for classification of diseases including receiving image data associated with an image at a first resolution. The image may be processed, for example by removing a background from the image, deconstructing the image into separate layers, and segmenting the image to define a plurality of single-cell images. A single-cell image may be processed, for example, by applying a filter to the single-cell image to decrease a resolution of the single-cell image as compared to the first resolution, to a second resolution. A label may be assigned to the single-cell image. A machine learning model is trained to predict a classification of the single-cell image based on inputting a plurality of single-cell images into the model. The trained machine learning model may be used to predict the outcome of a treatment. Systems and computer program products are also provided.