Author name cluster

Siyeop Yoon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ICML Conference 2025 Conference Paper

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective

Yujin Oh
Pengfei Jin
Sangjoon Park
Sekeun Kim
Siyeop Yoon
Jinsung Kim
Kyungsang Kim
Xiang Li 0001

Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e. g. , age, sex, race) and clinical factors (e. g. , disease severity). To address these challenges, we introduce Distribution-aware Mixture of Experts (dMoE), inspired by optimal control theory. We provide a comprehensive analysis of its underlying mechanisms and clarify dMoE’s role in adapting to heterogeneous distributions in medical image segmentation. Furthermore, we integrate dMoE into multiple network architectures, demonstrating its broad applicability across diverse medical image analysis tasks. By incorporating demographic and clinical factors, dMoE achieves state-of-the-art performance on two 2D benchmark datasets and a 3D in-house dataset. Our results highlight the effectiveness of dMoE in mitigating biases from imbalanced distributions, offering a promising approach to bridging control theory and medical image segmentation within fairness learning paradigms. The source code is available at https: //github. com/tvseg/dMoE.

Details

NeurIPS Conference 2025 Conference Paper

System-Embedded Diffusion Bridge Models

Bartlomiej Sobieski
Matthew Tivnan
Yuang Wang
Siyeop yoon
Pengfei Jin
Dufan Wu
Quanzheng Li
Przemyslaw Biecek

Solving inverse problems—recovering signals from incomplete or noisy measurements—is fundamental in science and engineering. Score-based generative models (SGMs) have recently emerged as a powerful framework for this task. Two main paradigms have formed: unsupervised approaches that adapt pretrained generative models to inverse problems, and supervised bridge methods that train stochastic processes conditioned on paired clean and corrupted data. While the former typically assume knowledge of the measurement model, the latter have largely overlooked this structural information. We introduce System-embedded Diffusion Bridge Models (SDBs), a new class of supervised bridge methods that explicitly embed the known linear measurement system into the coefficients of a matrix-valued SDE. This principled integration yields consistent improvements across diverse linear inverse problems and demonstrates robust generalization under system misspecification between training and deployment, offering a promising solution to real-world applications.

PDF Details

IROS Conference 2016 Conference Paper

Expeditious design optimization of a concentric tube robot with a heat-shrink plastic tube

Gunwoo Noh
Siyeop Yoon
Sung Yoon
Keri Kim
Woosub Lee
Sungchul Kang
Deukhee Lee

Design optimization and fabrication of concentric tube robots are time consuming because of the complexity of their workspaces and the characteristics of the superelastic materials used to make them. This paper presents a procedure for the expeditious design and fabrication of a concentric tube robot for applications that require rapid tube preparation but have less complex design constraints. This procedure reduces a 3D workspace optimization problem to a 2D problem. The continuum robot includes a heat-shrink tube to reduce fabrication time and to give it a small radius of curvature. Experimental results illustrate the feasibility of the proposed procedure.

Details