Join us on June 9 for a virtual workshop to learn how to handle expert label disagreement and build high performing fine-tuned medical foundation models for clinical imaging tasks. Register for the Zoom 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.…