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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning-enabled medical computer vision.

Andre Esteva1, Katherine Chou2, Serena Yeung3

  • 1Salesforce AI Research, San Francisco, CA, USA. andre.esteva@gmail.com.

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|January 9, 2021
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Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning computer vision advance medical applications. These technologies offer insights from medical imaging and video, improving patient care and clinical workflows.

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Computer Vision

Background:

  • A decade of significant advancements in artificial intelligence (AI) highlights its potential to revolutionize medicine.
  • Deep learning-powered computer vision techniques are increasingly being developed for medical applications.

Purpose of the Study:

  • To survey recent progress in deep learning computer vision for medical applications.
  • To explore applications in medical imaging, medical video analysis, and clinical deployment.
  • To identify challenges and future directions for integrating these technologies into healthcare.

Main Methods:

  • Review of deep learning advancements, particularly convolutional neural networks (CNNs).
  • Analysis of computer vision applications across various medical specialties.
  • Discussion of integration strategies for clinical workflows and deployment hurdles.

Main Results:

  • Demonstrated success of CNNs in various vision tasks within healthcare.
  • Identified key medical imaging applications benefiting from AI, including cardiology, pathology, dermatology, and ophthalmology.
  • Highlighted potential for computer vision to enhance medical video analysis and clinical workflows.

Conclusions:

  • Deep learning computer vision holds substantial promise for transforming medical diagnostics and patient care.
  • Real-world clinical deployment requires addressing significant challenges and hurdles.
  • Continued research and development are crucial for realizing the full potential of AI in medicine.