US Patent Application 17896829. METHOD FOR DETECTING MEDICAL IMAGES, ELECTRONIC DEVICE, AND STORAGE MEDIUM simplified abstract

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METHOD FOR DETECTING MEDICAL IMAGES, ELECTRONIC DEVICE, AND STORAGE MEDIUM

Organization Name

HON HAI PRECISION INDUSTRY CO., LTD.

Inventor(s)

TZU-CHEN Lin of New Taipei (TW)

METHOD FOR DETECTING MEDICAL IMAGES, ELECTRONIC DEVICE, AND STORAGE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 17896829 titled 'METHOD FOR DETECTING MEDICAL IMAGES, ELECTRONIC DEVICE, AND STORAGE MEDIUM

Simplified Explanation

The patent application describes a method for detecting medical images using an electronic device. Here are the key points:

  • The method involves obtaining medical images to be detected.
  • A reconstructed image is generated by inputting the target image into a pre-trained variational autoencoder model.
  • The pixel values of the reconstructed image and the target image are used to determine a target area.
  • The target image is then inputted into a pre-trained convolutional neural network model to obtain a feature area and a lesion category.
  • If there is a feature area corresponding to the target area, a lesion area and corresponding lesion category are determined.
  • Finally, a detection result of the image to be detected is generated based on the determined lesion area and category.


Original Abstract Submitted

A method for detecting medical images implemented in an electronic device includes obtaining at least one image to be detected; obtaining a reconstructed image by inputting the at least one image to be detected as a target image into a pre-trained variational autoencoder model; determining a target area according to pixel values of pixels in the reconstructed image and the target image; obtaining a feature area and a lesion category of the feature area by inputting the target image into a pre-trained convolutional neural network model; when there is a feature area corresponding to the target area in the target image, determining a lesion area and a corresponding lesion category based on the target area and the feature area, and generating a detection result of the image to be detected.

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