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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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Isolation of Cancer Stem Cells From Human Prostate Cancer Samples
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Prostate cancer detection using residual networks.

Helen Xu1, John S H Baxter2, Oguz Akin3

  • 1Ezra AI Canada, Unit 310, 545 King St. West, Toronto, Canada.

International Journal of Computer Assisted Radiology and Surgery
|April 12, 2019
PubMed
Summary
This summary is machine-generated.

A deep learning model using residual networks can accurately detect prostate cancer lesions on MRI scans, achieving 93% accuracy in identifying suspicious regions. This advancement aids in precise prostate cancer diagnosis.

Keywords:
Deep learningLesion segmentationMulti-parametric MRIProstate cancer

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in radiology

Background:

  • Prostate cancer diagnosis relies on accurate interpretation of multi-parametric magnetic resonance images (mp-MRI).
  • Manual segmentation of suspicious lesions by radiologists is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and evaluate an automated method for identifying suspected prostate cancer regions on mp-MRI.
  • To leverage deep learning for improved efficiency and accuracy in prostate cancer detection.

Main Methods:

  • A residual network architecture was employed for image segmentation.
  • The network was trained using segmentations from an expert radiologist.
  • Input data included T2-weighted, apparent diffusion coefficient (ADC) map, and high b-value diffusion-weighted images from 346 patients.

Main Results:

  • The residual network achieved a 93% hit-or-miss accuracy for lesion detection.
  • An average Jaccard score of 71% indicated strong agreement between the network's and the radiologist's segmentations.
  • The model demonstrated proficiency in learning relevant image features for prostate lesion identification.

Conclusions:

  • Residual networks show significant potential for automated prostate lesion segmentation on mp-MRI.
  • This technology can assist radiologists in detecting prostate cancer, potentially improving diagnostic workflows.
  • Further validation is warranted to integrate this AI tool into clinical practice.