Institution profile

University of Copenhagen

Academic institutioneurope · dk
Official website
Research library756linked papers
Opportunities0open roles
Selected work

Representative Papers

人工智能醫學應用的前景與風險

Jan 01, 2019International Journal of Chinese & Comparative Philosophy of Medicine

This study critically examines AI’s dual impact in healthcare: its transformative potential in genomics and public health, alongside profound ethical and institutional risks—including privacy breaches, algorithmic bias, physician deskilling, and imbalanced human–machine decision authority. Moving beyond technocentric paradigms, it introduces two foundational conceptual contributions: the reconfiguration of care as “datafied caregiving” and the normative calibration of “machine recommendation weight,” both grounded in philosophy of technology and bioethics. Employing an interdisciplinary analytical framework integrating medical ethics, philosophy of science, health policy, and big-data governance, the study uncovers structurally embedded risks overlooked in prevailing discourse. Its key contribution lies in reframing global regulatory and ethics review frameworks to center transparency, redistribution of epistemic and decisional authority, and preservation of clinical agency as core evaluative criteria.

18 citations2 influentialRead paper

Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges

Nov 15, 2022J. Am. Medical Informatics Assoc.

This paper identifies and systematically analyzes two critical ethical challenges arising from adaptive machine learning systems in clinical practice: *diachronic drift*—temporal shifts in model behavior over time—and *synchronic variation*—inter-institutional disparities in algorithmic behavior across deployments—both threatening patient safety, validity of informed consent, and healthcare equity. Methodologically, the study integrates clinical scenario modeling with cross-institutional comparative analysis of algorithmic behavior, grounded in a medical AI governance framework. It is the first to formally define and foreground synchronic variation as an ethically distinct concern for quality assurance, regulatory compliance, and algorithmic fairness. The contribution fills a key gap in medical AI ethics research by proposing two foundational ethical principles—*diachronic stability* and *synchronic consistency*—and delivering an actionable ethical risk assessment guide tailored for developers, regulators, and clinicians.

10 citationsRead paper

A Systematic Comparison of Syntactic Representations of Dependency Parsing

May 29, 2017UDW@NoDaLiDa

This study systematically investigates how dependency annotation schemes affect the performance of transition-based parsers. Method: Addressing language-specific non-canonical structures in Universal Dependencies (UD) treebanks, we design standardization transformation rules and comparatively evaluate parser performance—measured by LAS and UAS—under both original and standardized annotations within a unified, multilingual evaluation framework. Contribution/Results: We empirically demonstrate, for the first time, that annotation standardization does not universally improve parsing accuracy. Crucially, we reveal that linguistic typological features significantly moderate the effectiveness of annotation schemes: for certain languages, the original non-standard annotations yield higher accuracy than standardized ones. This finding challenges the implicit assumption that standardization is inherently optimal and underscores the necessity of considering language-specific syntactic properties when selecting or designing syntactic representations.

6 citationsRead paper

Minimum Star Partitions of Simple Polygons in Polynomial Time

Nov 17, 2023Symposium on the Theory of Computing

This paper resolves the long-standing “minimum star-shaped partition of a simple polygon” problem—open since 1981—by covering a given simple polygon with the fewest non-overlapping star-shaped subpolygons, allowing Steiner points. The proposed method integrates geometric decomposition, visibility graph optimization, dynamic programming, and structural analysis of star kernels, constructing the DP state space over triangulations. It yields the first exact polynomial-time algorithm applicable to arbitrary simple polygons, overcoming prior restrictions to monotone or orthogonal polygons and eliminating the requirement to forbid Steiner points. The algorithm runs in O(n⁹) time, a substantial improvement over exponential brute-force approaches. This theoretical breakthrough enables direct applications in CNC pocket milling, motion planning, and shape parameterization, where minimal star-shaped decompositions are essential for efficient toolpath generation, collision-free navigation, and domain mapping.

4 citations1 influentialRead paper

NUDF: Neural Unsigned Distance Fields for High Resolution 3D Medical Image Segmentation

Mar 28, 2022IEEE International Symposium on Biomedical Imaging

High-resolution 3D medical image segmentation faces dual challenges of memory bottlenecks and fine-detail loss, especially for topologically complex and morphologically variable structures such as the left atrial appendage. To address this, we propose Neural Unsigned Distance Fields (NUDF), the first method to introduce neural implicit distance fields into medical image segmentation. NUDF employs a coordinate-encoded MLP to directly learn a continuous unsigned distance field from raw CT volumes, thereby avoiding downsampling artifacts and memory constraints inherent to discrete voxel grids. It enables high-fidelity 3D mesh reconstruction with arbitrary topology—including open surfaces—and incorporates continuous distance-based supervision alongside end-to-end differentiable mesh extraction. Evaluated on left atrial appendage segmentation in CT, NUDF achieves sub-voxel accuracy (mean surface error ≈ voxel spacing), significantly outperforming conventional discrete voxel-based methods while reducing memory consumption by an order of magnitude.

4 citations1 influentialRead paper
Recent publications

Latest Papers

Stuffed IBLTs: Optimal Linear Multiset Sketches

Sep 15, 2026

该论文提出了一种名为Stuffed IBLT的线性草图方法，用于精确恢复原始向量。此方法在保持信息理论最优空间使用的同时，支持高效的更新和解码操作，解决了多集合协调问题。

0 citationsRead paper