20240054640. System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images simplified abstract (CARNEGIE MELLON UNIVERSITY)

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System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images

Organization Name

CARNEGIE MELLON UNIVERSITY

Inventor(s)

Yongxin Zhao of Sewickley PA (US)

Christopher Byungjun Ahn of Loveland OH (US)

System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240054640 titled 'System, Method, and Computer Program Product for Classification of Diseases Based on Expansion Microscopic Images

Simplified Explanation

The method described in the patent application involves classifying diseases using image data and machine learning.

  • Image data associated with an image is received at a first resolution.
  • The image is processed, which may include removing background, deconstructing into layers, and segmenting into single-cell images.
  • Single-cell images are processed by applying a filter to decrease resolution to a second resolution and assigning a label.
  • A machine learning model is trained to predict the classification of single-cell images.
  • The trained model can be used to predict treatment outcomes.
    • Potential Applications:**

- Medical diagnosis and treatment prediction - Disease classification in pathology and radiology

    • Problems Solved:**

- Efficient and accurate disease classification - Predicting treatment outcomes based on image data

    • Benefits:**

- Improved accuracy in disease classification - Personalized treatment predictions based on image analysis


Original Abstract Submitted

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.

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