Sidebar

  • Magazine
  • Events
  • Videos
  • Gallery
  • Blog
  • Gantry Home

Magazine menu

  • Home
  • News
    • China News
    • Religion
  • lifestyle
  • Tech
  • Financial
  • Military
  • Entertainment
  • Politics
  • Health
  • Sport
  • Environment
  • Opinion
  • Weather
  • Podcasts
  • Video
  • Ads
The Power of Truth®
Thursday, July 03, 2025
Thursday, July 03, 2025
  • Home
  • News
    • China News
    • Religion
  • lifestyle
  • Tech
  • Financial
  • Military
  • Entertainment
  • Politics
  • Health
  • Sport
  • Environment
  • Opinion
  • Weather
  • Podcasts
  • Video
  • Ads
  1. You are here:  
  2. Health

New AI method captures uncertainty in medical images

Details
Staff logo
11 April 2024
Health
  • Previous Article Economic burden of childhood verbal abuse by adults estimated at $300 billion globally
  • Next Article Scientists uncover key resistance mechanism to Wnt inhibitors in pancreatic and colorectal cancers
Tyche is a machine-learning framework that can generate plausible answers when asked to identify potential disease in medical images. By capturing the ambiguity in images, the technique could prevent clinicians from missing crucial information that could inform diagnoses.
Tyche is a machine-learning framework that can generate plausible answers when asked to identify potential disease in medical images. By capturing the ambiguity in images, the technique could prevent clinicians from missing crucial information that could inform diagnoses.

Read more https://www.sciencedaily.com/releases/2024/04/240411130304.htm

  • Previous Article Economic burden of childhood verbal abuse by adults estimated at $300 billion globally
  • Next Article Scientists uncover key resistance mechanism to Wnt inhibitors in pancreatic and colorectal cancers

HUNGRY FOR TRUTH?  FEED THE NEED.

The Power of Truth®
  • Cookies Policy
  • Privacy Policy
  • Terms of Use
  • Contact
Copyright © 2025 Joomla!. All Rights Reserved. Powered by The Power of Truth® - Designed by JoomlArt.com. Bootstrap is a front-end framework of Twitter, Inc. Code licensed under Apache License v2.0. Font Awesome font licensed under SIL OFL 1.1.