Colorectal cancer image classification method and system combining deep learning and radiomics

A technology of colorectal cancer and radiomics, applied in the field of medical image processing and image classification, can solve the time-consuming and labor-intensive problems of manual marking by doctors, and achieve the effect of alleviating the small amount of data

Pending Publication Date: 2022-04-12
FUZHOU UNIV
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Problems solved by technology

[0010] 2. In order to solve the time-consuming and labor-intensive problem of manual marking by doctors, the automatic segmentation network model of deep learning is introduced to automatically mark the region of interest from the image

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  • Colorectal cancer image classification method and system combining deep learning and radiomics
  • Colorectal cancer image classification method and system combining deep learning and radiomics
  • Colorectal cancer image classification method and system combining deep learning and radiomics

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[0043]In order to make the features and advantages of this patent more obvious and easy to understand, the following special examples are described in detail as follows:

[0044] Such as figure 1 As shown, the method and system for colorectal cancer image classification combined with deep learning and radiomics proposed in this embodiment specifically includes the following schemes:

[0045] (1) Data preprocessing: use data enhancement methods (rotation, translation, image transformation, etc.) to fully amplify the existing data; use the existing manually labeled data to train the segmentation network, and use the trained model from The region of interest is automatically segmented in the image to obtain more data with annotations.

[0046] (2) Feature extraction: The relevant omics features are extracted from abdominal CT through the open source Python software package Pyradiomics; the resnet training model selects the model with the best results, and uses this model to extr...

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Abstract

The invention provides a colorectal cancer image classification method and a colorectal cancer image classification system combining deep learning and imageomics, aiming at the problem of training a small sample by a deep learning model, the existing data is fully utilized to carry out data enhancement (rotation, translation, image transformation and the like) so as to solve the problem of time and labor consumption caused by manual marking by a doctor. An automatic segmentation network model of deep learning is introduced, and the region of interest is automatically marked from the image. In order to solve the problems that the explainability of the features extracted by the deep learning model is poor and the obtained feature information is not comprehensive, more and more comprehensive feature information is obtained by fusing the image omics features, the deep learning features and the clinical pathological information, and the classification accuracy and reliability of the image omics are further improved.

Description

technical field [0001] The invention belongs to the technical field of medical image processing and image classification, and in particular relates to a colorectal cancer image classification method and system combining deep learning and radiomics. Background technique [0002] 1. Radiomics program: radiomics mainly uses computer software to extract a large number of high-dimensional quantitative image features from CT, MRI and PET images at high throughput; the process includes data collection, image segmentation, feature extraction, and feature selection And model building, by virtue of deeper mining, prediction and analysis of massive image data information to assist doctors to make the most accurate image classification. In practical applications, radiomics lesions need to be manually marked by doctors, which is time-consuming and subject to subjective bias. In addition, the lack of a standardized and standard process and quality control system when using quantitative m...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/80G06V10/764G06V10/771G06V10/25G06V10/44G06V10/82G06K9/62G06N3/04G06N3/08G06N20/20
Inventor 黄立勤何甜潘林郑绍华
Owner FUZHOU UNIV
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