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VERSION:2.0
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CALSCALE:GREGORIAN
X-WR-CALNAME:IBIIS & AIMI Seminar - NCI Imaging Data Commons: Towards Trans
 parency\, Reproducibility\, and Scalability in Imaging AI
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260520T040601Z
UID:tag:localist.com\,2008:EventInstance_45862571125919
DTSTART:20240320T190000Z
DTEND:20240320T200000Z
DESCRIPTION:Andrey Fedorov\, PhD \nAssociate Professor\, Harvard Medical Sc
 hool\nLead Investigator\, Brigham and Women's Hospital\n\nTitle: NCI Imagi
 ng Data Commons: Towards Transparency\, Reproducibility\, and Scalability 
 in Imaging AI\n\nAbstract\nThe remarkable advances of artificial intellige
 nce (AI) technology are revolutionizing established approaches to the acqu
 isition\, interpretation\, and analysis of biomedical imaging data. Develo
 pment\, validation\, and continuous refinement of AI tools requires  easy 
 access to large high-quality annotated datasets\, which are both represent
 ative and diverse. The National Cancer Institute (NCI) Imaging Data Common
 s (IDC) hosts over 50 TB of diverse publicly available cancer image data s
 panning radiology and microscopy domains. By harmonizing all  data based o
 n industry standards and colocalizing it with analysis and exploration res
 ources\, IDC aims to facilitate the development\, validation\, and clinica
 l translation of AI tools and address the well-documented challenges of es
 tablishing reproducible and  transparent AI processing pipelines. Balanced
  use of established commercial products with open-source solutions\, inter
 connected  by standard interfaces\, provides value and performance\, while
  preserving sufficient agility to address the evolving needs of the resear
 ch community. Emphasis on the development of tools\, use cases to demonstr
 ate the utility of uniform data representation\, and  cloud-based analysis
  aim to ease adoption and help define best practices. Integration with oth
 er data in the broader NCI Cancer Research Data Commons infrastructure ope
 ns opportunities for multiomics studies incorporating imaging data to furt
 her empower the research community to accelerate breakthroughs in cancer d
 etection\, diagnosis\, and treatment. The presentation will discuss the re
 cent developments in IDC\, highlighting resources\, demonstrations and exa
 mples that we hope can help you improve your everyday imaging research pra
 ctices - both those that use public and internal datasets.
GEO:37.431462;-122.174561
LOCATION:Clark Center\, S360
SUMMARY:IBIIS & AIMI Seminar - NCI Imaging Data Commons: Towards Transparen
 cy\, Reproducibility\, and Scalability in Imaging AI
URL;VALUE=URI:https://events.stanford.edu/event/ibiis-aimi-seminar-nci-imag
 ing-data-commons-towards-transparency-reproducibility-and-scalability-in-i
 maging-ai
CATEGORIES:Class/Seminar
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