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Related Experiment Video

Updated: Oct 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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Deep learning applied to breast imaging classification and segmentation with human expert intervention.

Rory Wilding1, Vivek M Sheraton1, Lysabella Soto2,3

  • 1Onkolyze Pte. Ltd., Singapore, Singapore.

Journal of Ultrasound
|January 9, 2022
PubMed
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Insights into imaging·2026

Machine learning algorithms accurately classify breast ultrasounds, distinguishing healthy from abnormal tissue with 96% accuracy and benign from malignant tumors with 85% accuracy, improving cancer diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer diagnosis relies on accurate interpretation of ultrasound images.
  • Automated analysis can enhance diagnostic efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for automatic classification and segmentation of breast tumors in ultrasound images.
  • To improve the accuracy of breast ultrasound assessments for better patient diagnosis and treatment planning.

Main Methods:

  • Collected 953 breast ultrasound images from open-source datasets.
  • Classified images into normal, benign, and malignant categories with radiologist input.
  • Developed machine learning algorithms for image classification and tumor segmentation.
Keywords:
Artificial intelligenceBIRADSDynamic U-netSegmentation

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Related Experiment Videos

Last Updated: Oct 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

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Published on: November 11, 2022

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Main Results:

  • Classification algorithms achieved 96% accuracy in distinguishing healthy from abnormal tissue and 85% accuracy for benign versus malignant tumors.
  • Segmentation algorithm achieved an 80.31% DICE score for mass identification.
  • Identified 3.92% erroneous classifications in the original datasets.

Conclusions:

  • Deep learning algorithms show significant potential for enhancing the accuracy of breast ultrasound assessments.
  • Automated analysis of breast ultrasounds can facilitate more efficient and reliable diagnostic processes.