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SIIM26 logo footer
  • Attend
    • Hotels + Venue
    • Who Should Attend?
    • Networking Events
    • Justify Your Attendance!
    • Preliminary Program
  • Program
    • Program Agenda
    • Featured Education
      • General Sessions
      • Learning Labs
    • Roadmaps
      • Cardiology
      • Dermatology
      • Ophthalmology
      • Pathology
      • Radiology
    • Continuing Education
    • Speakers List
    • Preliminary Program
  • TECH Expo
    • Exhibitors
    • Alphabetical Exhibitor List
    • Product Categories
    • Floor Plan
  • Pre-Conference
    • Epic Users Group Meeting
    • SIIM-ACR Data Science Summit
  • Exhibit + Sponsor
    • How To Sign Up
    • Sponsor + Advertise
    • Startup Street
    • Booth Contract
    • Rules & Regulations
    • Vendor Prospectus
    • Exhibitor Resources
    • Suitcase Marketing Policy
  • Register
Dr. Tyler Bradshaw, a leading expert in medical imaging, attends the SIIM Annual Meeting to discuss innovations in radiology and healthcare technology.
Event Speaker

Tyler Bradshaw, PhD

Associate Professor
University of Wisconsin - Madison

Dr. Bradshaw is an Associate Professor at the University of Wisconsin-Madison in the Department of Radiology. His lab focuses on using artificial intelligence to automate the analysis and interpretation of medical images. They explore novel methods for combining medical image analysis and language modeling. Dr. Bradshaw has served on the Board of Directors for the Physics, Instrumentation, and Data Science Council (PIDSC) of the SNMMI and co-leads its Artificial Intelligence Task Force. Dr. Bradshaw also co-leads the NucLex Committee, an effort to standardize nuclear medicine terminology. Dr. Bradshaw’s clinical duties include overseeing nuclear medicine physics support, including radiopharmaceutical dosimetry.

Sessions

AI Ecosystems in Imaging Scientific Research Abstracts

Presented by Preetham Bachina, MSE, Tyler Bradshaw, PhD, Mitchell Goldburgh, FSIIM, BSCS, Aditya Kulkarni, MSc., Mana Moassefi, MD in Pathology, Radiology Jun 10, 2026 | 5:15 PM.

Data Hygiene at Scale: A Contrastive Learning Framework for Auditing Data Provenance and Overlap Aditya Kulkarni, MS, Research Engineer, St. Jude Children's Research Hospital Khushang Zaveri; Dharmam Savani, MSc; Ammar Ahmed Pallikonda Latheef, MSc; Pritam Mukherjee, PhD; Jeremias Sulam, PhD; Paul Yi, MD Lost in Translation: International Limitations of Deep Learning Demographic Classifiers Developed on…

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19440 Golf Vista Plaza
Ste 330
Leesburg, VA 20176
703-723-0432
info@siim.org

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