Hyperion Research LogoHyperion Research Logo_StickyHyperion Research LogoHyperion Research Logo
  • Home
  • Services
    • Traditional and Emerging HPC
    • HPC User Forum
    • Worldwide High Performance Technical Server QView
    • Worldwide HPC Server, Verticals and Countries Forecast Database
    • High Performance Data Analysis-Artificial Intelligence (HPDA-AI)
    • Cloud Computing Program
    • Consulting Services
    • Quantum Computing Continuing Information Service
    • High-Performance Computing Pathfinders
    • HPC End-User Multi-Client Study 2022
  • Team
  • Sample Projects
    • Research Plan
    • List of Recent Reports
    • To Out-compute is to Out-compete: Competitive Threats and Opportunities Relative to U.S. Government HPC Leadership
    • HPC-AI Success Story
    • HPC Market Update during SC22
    • Taxonomy
      • AI-HPDA Taxonomy
      • HPC Server Tracking and Application Workload Segments
      • Traditional HPC and AI-HPDA Subverticals
    • NERSC Update, May 2021 HPC User Forum
    • Cloud Computing Changing HPC Spending
    • NASA Bespoke HPC Study
    • ROI with HPC
    • Interview Series
    • Cloud Application Assessment Tool
    • MCS Server Highlights 2021
    • QC User Study 2021
    • HPC Storage Review 2021 First Half Yr
    • Hyperion Research Sponsored Tech Spotlight AMD-Supermicro
  • Events
  • Contact
0

$0.00

LOGIN
✕
  • Home
  • HYP_Link
  • Deep Transfer Learning Framework Applied to Radiation Therapy
Awaiting product image

Deep Transfer Learning Framework Applied to Radiation Therapy

$1,500.00

Authors: Alex Norton, Tom Sorensen

Publication Date: 2 202022

Length: 1 pages

Category: HYP_Link
Share
Description

At the conclusion of 2021, researchers at UNC Charlotte and Duke University Medical Center published results of work done to use transfer learning methods to generate fluence maps for radiation therapy, aimed at providing medical professionals with more capability and information in fighting adrenal cancers. The technique uses a deep transfer learning model trained on a much larger dataset that can be applied to a smaller data set for a specific application.

  • The initial model was trained on pancreas treatment plans, then retuned and applied to a smaller set of data points on adrenal cancers. The output of the model generates a fluence map for specific IMRT beam-based treatments for adrenal cancers.
  • According to the researchers, this approach is meant to supplement but not replace human expertise in the field and is reliant on human expertise to finetune and improve the AI model.

Related Products

    European Union Seeking to Strengthen Semiconductor Ecosystem

    Mark Nossokoff, Bob Sorensen

    On February 8, 2022, the European Commission formally proposed what's commonly referred to as the European Chips Act. The legislation plans to build on Europe's strengths and address weaknesses to develop a thriving domestic semiconductor ecosystem and resilient supply chain, while setting measures to anticipate and respond to future supply chain disruptions. In the short term, the Act seeks to bolster EU capabilities to anticipate future chips crises, strengthen manufacturing activities in the EU, and support scale-up and innovation across the whole value chain. In the mid- to long-term, it seeks to reinforce Europe's technological leadership while developing mechanisms to support transfer of knowledge from the lab to the fab and position Europe as a technology leader in innovative downstream markets.

    2 202022 | HYP_Link

    Barcelona Supercomputing Center Trains Spanish NLP Model

    Alex Norton, Bob Sorensen

    Recently, the Barcelona Supercomputing Center (BSC) trained the first large artificial intelligence (AI) model designed to understand, speak, and write in the Spanish language. The system, named MarIA, was trained on the MareNostrum supercomputer at the BSC, leveraging 59 TBs of language data from the Biblioteca Nacional de España, one of the world's largest public libraries. The model is said to be an expert in both writing and understanding the Spanish language and is free to use by any developer, company, or entity. The system has a wide variety of potential applications including summarization applications, chatbots, smart searches, translation engines, and automatic subtitling chatbots.

    9 202021 | HYP_Link

Have any questions?

365 Summit Ave.
St. Paul MN 55102, USA.

info@hyperionres.com

© 2021 Hyperion Research. All Rights Reserved | Privacy Policy | Website Terms of Use
LOGIN
0

$0.00

✕

Login

Lost your password?