{"type":"ScholarlyArticle","title":"Two-Fold Patch Perturbation for Efficient Self-Supervised Learning in 3D Medical Imaging","authors":[{"name":"Tirthajit Baruah","slug":"tirthajit-baruah","given":"Tirthajit","family":"Baruah","affiliation":""},{"name":"Kabir Jamadar","slug":"kabir-jamadar","given":"Kabir","family":"Jamadar","affiliation":""},{"name":"Punit Rathore","slug":"punit-rathore","given":"Punit","family":"Rathore","affiliation":""}],"abstract":"Self-supervised pre-training has become a key paradigm for reducing annotation costs in 3D medical imaging, yet many recent approaches rely on complex objectives or incur substantial computational overhead. We propose a simple and efficient self-supervised pre-training framework for 3D medical images based on a two-fold patch-wise perturbation strategy. The method applies Bernoulli patch masking and discrete rotations, and trains a shared encoder with a three-head objective for reconstruction, perturbation localization, and rotation prediction. This design encourages spatially aware and transferable representations while remaining computationally lightweight. Experiments across diverse segmentation and classification benchmarks, including modality-shift scenarios, demonstrate consistent improvements over general self-supervised baselines and competitive or superior performance compared to recent medical self-supervised methods, while requiring substantially less memory, computation, and training time than the state-of-the-art pre-training pipelines.","doi":"10.24963/ijcai.2026/740","url":"https://ijcai.org/proceedings/2026/740","pdf":"https://ijcai.org/proceedings/2026/0740.pdf","markdown":"https://ijcai.org/proceedings/2026/740.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/740","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":740,"pages":{"first":6654,"last":6662},"keywords":["Medical knowledge representation: Medical knowledge representation","Self-supervised learning: Self-supervised learning","Medical imaging: Medical imaging","Medical diagnosis: Medical diagnosis"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}