Visual AI in Healthcare: Ground Truth in the Foundation-Model Era

Virtual

Americas

CV Meetups

Healthcare

Visual AI in Healthcare: Ground Truth in the Foundation-Model Era - June 6, 2026

Jun 06, 2026

9:00 AM – 10:30 AM PST

Online. Register for the Zoom!

About this event

Learn how to handle expert label disagreement and build high performing fine-tuned medical foundation models for clinical imaging tasks. View more CV events here.

Host

Medical imaging teams are increasingly fine-tuning foundation models like UNI, MedSAM2, and BiomedCLIP on small in-house datasets. At that scale, label disagreement is a dominant cause of model failures, and the disputed ground truth is what regulators will ask you to defend. We'll build a medical imaging dataset in FiftyOne, surfacing and analyzing the cases where reviewers disagree. From there, we'll fine-tune a foundation model on cleaned data and use FiftyOne to evaluate where our model succeeds and fails, and which data is needed to move the model’s performance forward.

You’ll learn how to:

Build a medical imaging dataset that preserves multiple expert annotations as first-class fields
Use FiftyOne views, embedding similarity, and confidence-disagreement signals to find the samples where reviewers split.
Run label-quality screens, near-duplicate detection, and active-learning sample selection using foundation model embeddings
Fine-tune a medical foundation model on a defensible dataset, with auditable and versioned experiment tracking
Filter and slice evaluation for regulatory and clinical readiness
Drive the pipeline with natural-language agents using the FiftyOne MCP Server and Skills to run the same curation, evaluation, and review workflows from your favorite AI tool

Who This Is For

ML and computer-vision engineers in the medical imaging space
Data and annotation operations teams
Clinical AI and digital pathology leads
Regulatory and quality leads

Register

Save your seat for this live session. Attendees will receive a copy of all slides, notebooks, and workshop materials.