{"type":"ScholarlyArticle","title":"RF-DTR: A Multi-Stage DCT Token Regression Network for Progressive Rib Fracture Mask Refinement","authors":[{"name":"ShouYu Chen","slug":"shouyu-chen","given":"ShouYu","family":"Chen","affiliation":""},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":""},{"name":"JunTao Wang","slug":"juntao-wang","given":"JunTao","family":"Wang","affiliation":""},{"name":"Usman Naseem","slug":"usman-naseem","given":"Usman","family":"Naseem","affiliation":""},{"name":"Zhongyuan Lai","slug":"zhongyuan-lai","given":"Zhongyuan","family":"Lai","affiliation":""},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":""}],"abstract":"Rib fracture patterns are key indicators of trauma severity. Detecting and locating these fractures is a critical yet time-consuming task, especially in 3D imaging, due to their minute size and irregular geometries. Existing voxel-based spatial methods fail to capture frequency-domain variations inherent in imaging and do not replicate the progressive refinement process used by clinicians during manual annotation, leading to suboptimal results. We propose a novel regression network, RF-DTR, incorporating a gated regressor mechanism and operating entirely in the frequency domain to address these challenges. Specifically, we present an innovative spatial-frequency transform applied to volumes and corresponding masks. Furthermore, we introduce a Mahalanobis regularization technique to enhance the model and learn high-frequency DCT components relevant to clinical tasks. Finally, a hierarchical penalty is proposed to improve the confidence of the prediction. Extensive experiments confirm our method's superiority in handling complex, sparsely annotated medical imaging datasets.","doi":"10.24963/ijcai.2025/1017","url":"https://ijcai.org/proceedings/2025/1017","pdf":"https://ijcai.org/proceedings/2025/1017.pdf","markdown":"https://ijcai.org/proceedings/2025/1017.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1017","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1017,"pages":{"first":9149,"last":9157},"keywords":["Domain-specific AI4Tech: AI4Care and AI4Health","Advanced AI4Tech: Data-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}