Lorenz Sparrenberg

Knowing When Not to Predict: Self Supervised Learning and Abstention for Safer DR Screening

Self-supervised learning (SSL) is now a standard way to pretrain medical image models, but performance is still mostly judged by downstream accuracy. For safety-critical screening tasks such as diabetic retinopathy grading, this is not enough: a model must also know when its predictions are unreliab