6 papers
Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation
Chao Wu, Mahesh Bhosale, Kangxian Xie +3
Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible. Modeling such inter-observer varia…
Learning Topology-Aware Implicit Field for Unified Pulmonary Tree Modeling with Incomplete Topological Supervision
Ziqiao Weng, Jiancheng Yang, Kangxian Xie +2
Pulmonary trees extracted from CT images frequently exhibit topological incompleteness, such as missing or disconnected branches, which substantially degrades downstream anatomical…
Refining 3D Medical Segmentation with Verbal Instruction
Kangxian Xie, Jiancheng Yang, Nandor Pinter +3
Accurate 3D anatomical segmentation is essential for clinical diagnosis and surgical planning. However, automated models frequently generate suboptimal shape predictions due to fac…
Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
Kangxian Xie, Yufei Zhu, Kaiming Kuang +4
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement…
Model-Agnostic Gender Bias Control for Text-to-Image Generation via Sparse Autoencoder
Chao Wu, Zhenyi Wang, Kangxian Xie +3
Text-to-image (T2I) diffusion models often exhibit gender bias, particularly by generating stereotypical associations between professions and gendered subjects. This paper presents…
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields
Kangxian Xie, Jiancheng Yang, Donglai Wei +2
Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the complex 3D tree-shape…