{"type":"ScholarlyArticle","title":"GeoSFLoRA: Geometry-Conditioned Spectral Flow Low-Rank Adaptation for 2D-to-3D Transfer in Medical Image Segmentation","authors":[{"name":"Qin Hao","slug":"qin-hao","given":"Qin","family":"Hao","affiliation":""},{"name":"Bonian Chen","slug":"bonian-chen","given":"Bonian","family":"Chen","affiliation":""},{"name":"Shengwei Tian","slug":"shengwei-tian","given":"Shengwei","family":"Tian","affiliation":""},{"name":"Long Yu","slug":"long-yu","given":"Long","family":"Yu","affiliation":""}],"abstract":"Accurate volumetric medical image segmentation is critical for clinical diagnosis, yet adapting two-dimensional vision foundation models (VFMs) to three-dimensional medical imaging remains challenging. While parameter-efficient fine-tuning (PEFT) methods such as LoRA and adapter-based schemes provide efficient alternatives to full fine-tuning, their geometry-agnostic parameter-space adaptations are insufficient to reconcile discrepancies induced by anisotropic voxel spacing and inter-slice discontinuities. We propose GeoSFLoRA, a geometry-constrained spectral-flow low-rank adaptation framework for efficient 2D-to-3D transfer learning. GeoSFLoRA adapts frozen 2D pretrained vision backbones by operating within a fixed low-rank spectral subspace induced by pretrained linear projections. For each adapted layer, a truncated singular value decomposition is computed once and kept frozen, preserving the original pretrained bases. A lightweight Geometry-Conditioned Encoder (GCE) extracts local volumetric descriptors, which are mapped to token-conditional residuals in the singular-value space, enabling bounded and geometry-aware spectral modulation. GeoSFLoRA consistently improves Dice and HD95 on BraTS20, MSD-Prostate, and MSD-Lung, approaching full fine-tuning performance and demonstrating an effective paradigm for 2D-to-3D medical image segmentation. The code is publicly available at https://github.com/chenbn266/GeoSFLoRA.","doi":"10.24963/ijcai.2026/748","url":"https://ijcai.org/proceedings/2026/748","pdf":"https://ijcai.org/proceedings/2026/0748.pdf","markdown":"https://ijcai.org/proceedings/2026/748.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/748","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":748,"pages":{"first":6725,"last":6733},"keywords":["Medical imaging: Medical imaging"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}