June 9 - Visual AI in Healthcare: Ground Truth in the Foundation-Model Era

Voxel51 is hosting a June 9 virtual workshop on building high-performing fine-tuned medical foundation models for clinical imaging tasks. The session will address how expert label disagreement becomes a dominant cause of model failures when fine-tuning models like UNI, MedSAM2, and BiomedCLIP on small datasets. Attendees will learn to surface and analyze disputed ground truth in FiftyOne, fine-tune a model on cleaned data, and evaluate performance to identify where additional data is needed.

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. 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: