{"type":"ScholarlyArticle","title":"Knowing When Not to Predict: Self Supervised Learning and Abstention for Safer DR Screening","authors":[{"name":"Muskaan Chopra","slug":"muskaan-chopra","given":"Muskaan","family":"Chopra","affiliation":""},{"name":"Lorenz Sparrenberg","slug":"lorenz-sparrenberg","given":"Lorenz","family":"Sparrenberg","affiliation":""},{"name":"Jan H. Terheyden","slug":"jan-h-terheyden","given":"Jan H.","family":"Terheyden","affiliation":""},{"name":"Rafet Sifa","slug":"rafet-sifa","given":"Rafet","family":"Sifa","affiliation":""}],"abstract":"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 pretraining influences confidence calibration and confidence-based abstention.\r\nWe evaluate multiple SSL checkpoints under a fixed fine-tuning protocol and assess calibrated confidence, coverage, selective accuracy, and selective macro-F1. Across datasets and data regimes, SSL pretraining improves selective prediction compared to training from scratch. Unlike prior SSL studies that primarily evaluate downstream accuracy or AUROC, we analyze how SSL pretraining duration influences calibration and selective prediction behavior under confidence-based abstention. However, once accuracy saturates, selective performance can still change markedly across checkpoints, and longer pretraining does not consistently improve reliability. These results underscore the importance of abstention-aware evaluation and suggest that pretraining length should be treated as an important reliability-related design choice rather than only a computational detail. Code is available at https://github.com/29\r\nmuskaan712/ijcai-knowing-when-not-to-predict.","doi":"10.24963/ijcai.2026/742","url":"https://ijcai.org/proceedings/2026/742","pdf":"https://ijcai.org/proceedings/2026/0742.pdf","markdown":"https://ijcai.org/proceedings/2026/742.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/742","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":742,"pages":{"first":6671,"last":6679},"keywords":["Public health: Public health","Medical diagnosis: Medical diagnosis","Medical knowledge representation: Medical knowledge representation","Self-supervised learning: Self-supervised learning","Medical imaging: Medical imaging"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}