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

ArXi:2605.19133v1 Announce Type: cross 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 unreliable and defer uncertain cases for clinical review. In this work, we examine how the length of SSL pre