Selected work

Representative Papers

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

May 01, 2023Comput. Biol. Medicine

Clinical monitoring of ascending aortic aneurysms (AAoA) suffers from low accuracy in predicting aneurysm growth rate using conventional radial measurements. Method: We propose a quantitative, 3D morphology–driven prediction framework integrating local and global geometric features. Specifically, we construct a robust shape representation by jointly encoding multi-scale surface curvature and topological invariants, and design a growth-rate–sensitive dynamic feature-weighting regression scheme. Implicit surface reconstruction and differential-geometric feature extraction are performed directly from clinical CT volumes, followed by LASSO-regularized gradient-boosted decision tree (GBDT) regression. Contribution/Results: Validated on a multicenter cohort, our method achieves a mean absolute error of 0.18 mm/yr in growth-rate prediction—37% lower than radial metrics—and an AUC of 0.89 for binary classification of fast versus slow growth. This work is the first to incorporate topological invariants into AAoA growth modeling, substantially improving the reliability of noninvasive, patient-specific risk assessment.

13 citationsRead paper

ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis

Jun 17, 2026

Head and neck cancer involves high-dimensional, heterogeneous multimodal clinical data with complex temporal dynamics, posing significant challenges for existing survival prediction methods to effectively model structured clinical workflows. This work proposes a clinical pathway-guided heterogeneous hierarchical directed graph framework that represents a patient’s care trajectory as a temporally aware sequence aligned with key diagnostic steps. By leveraging a hierarchical topology and a heterogeneous message-passing mechanism, the model flexibly integrates multimodal information while robustly handling missing data and asymmetric cross-modal relationships. Evaluated on two public datasets, the proposed approach achieves state-of-the-art discriminative performance and well-calibrated predictions. Ablation studies further confirm the contribution of each component to the overall efficacy of the framework.

0 citationsRead paper

AirCatch: Effectively tracing advanced tag-based trackers

Feb 07, 2026

This work addresses the challenge of illicit tracking via sophisticated tag trackers that evade detection by frequently rotating logical identifiers. To counter this, the authors propose AirCatch, a passive detection system leveraging physical-layer radio-frequency fingerprints. AirCatch exploits the stable carrier frequency offset (CFO) signatures inherent to transmitters and enhances device discriminability through modulation-aware CFO fingerprinting. It further introduces a contamination-resilient clustering algorithm based on high core density and persistence. Complementing the system, the authors develop BlePhasyr, an ultra-low-cost BLE software-defined radio receiver built from commodity hardware. Experimental results demonstrate that AirCatch achieves zero false positives and enables early detection across diverse device brands, realistic mobility scenarios, and high-intensity adversarial tests, with only marginal performance degradation under extreme conditions such as very low transmission rates that inherently diminish attack effectiveness.

0 citationsRead paper

On the parameterized complexity of the Maker-Breaker domination game

Jan 13, 2026

This study investigates the parameterized complexity of the Maker-Breaker domination game, which asks whether Dominator or Staller has a winning strategy on a given graph. Taking the number of moves required for either player to win as the parameter, the paper establishes that the problem of deciding whether Dominator can win within $k$ moves is W[2]-complete, while the analogous problem for Staller is W[1]-complete—marking the first such hardness results for this game. Furthermore, the authors develop fixed-parameter tractable (FPT) algorithms with respect to structural graph parameters, including neighborhood diversity, modular width, and $P_4$-sparseness. These contributions fully characterize the intractability of the game under move-count parametrization and enable efficient solutions under several natural structural restrictions.

0 citationsRead paper

Structured Spectral Graph Learning for Multi-label Abnormality Classification in 3D Chest CT Scans

Oct 12, 2025

Modeling long-range spatial dependencies in multi-label thoracic CT abnormality classification remains challenging due to the computational inefficiency of 3D CNNs and the heavy reliance of Vision Transformers (ViTs) on large-scale pretraining. Method: We propose a lightweight, graph-structured 2.5D approach: modeling CT volumes as weighted graphs whose nodes are axial slice triplets; employing spectral graph convolution to capture inter-slice long-range dependencies; and designing dedicated node representation learning, edge weighting, and multi-strategy graph aggregation mechanisms—eliminating the need for 3D convolutions or domain-specific large-scale pretraining. Contribution/Results: Our method achieves competitive classification performance with mainstream visual encoders across three independent multi-institutional datasets, significantly improving cross-center generalization. It further demonstrates strong transferability to radiology report generation and abdominal CT analysis, validating its effectiveness, robustness, and scalability.

0 citationsRead paper

Recent publications

Latest Papers

ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis

Jun 17, 2026

Head and neck cancer involves high-dimensional, heterogeneous multimodal clinical data with complex temporal dynamics, posing significant challenges for existing survival prediction methods to effectively model structured clinical workflows. This work proposes a clinical pathway-guided heterogeneous hierarchical directed graph framework that represents a patient’s care trajectory as a temporally aware sequence aligned with key diagnostic steps. By leveraging a hierarchical topology and a heterogeneous message-passing mechanism, the model flexibly integrates multimodal information while robustly handling missing data and asymmetric cross-modal relationships. Evaluated on two public datasets, the proposed approach achieves state-of-the-art discriminative performance and well-calibrated predictions. Ablation studies further confirm the contribution of each component to the overall efficacy of the framework.

0 citationsRead paper

AirCatch: Effectively tracing advanced tag-based trackers

Feb 07, 2026

This work addresses the challenge of illicit tracking via sophisticated tag trackers that evade detection by frequently rotating logical identifiers. To counter this, the authors propose AirCatch, a passive detection system leveraging physical-layer radio-frequency fingerprints. AirCatch exploits the stable carrier frequency offset (CFO) signatures inherent to transmitters and enhances device discriminability through modulation-aware CFO fingerprinting. It further introduces a contamination-resilient clustering algorithm based on high core density and persistence. Complementing the system, the authors develop BlePhasyr, an ultra-low-cost BLE software-defined radio receiver built from commodity hardware. Experimental results demonstrate that AirCatch achieves zero false positives and enables early detection across diverse device brands, realistic mobility scenarios, and high-intensity adversarial tests, with only marginal performance degradation under extreme conditions such as very low transmission rates that inherently diminish attack effectiveness.

0 citationsRead paper

On the parameterized complexity of the Maker-Breaker domination game

Jan 13, 2026

This study investigates the parameterized complexity of the Maker-Breaker domination game, which asks whether Dominator or Staller has a winning strategy on a given graph. Taking the number of moves required for either player to win as the parameter, the paper establishes that the problem of deciding whether Dominator can win within $k$ moves is W[2]-complete, while the analogous problem for Staller is W[1]-complete—marking the first such hardness results for this game. Furthermore, the authors develop fixed-parameter tractable (FPT) algorithms with respect to structural graph parameters, including neighborhood diversity, modular width, and $P_4$-sparseness. These contributions fully characterize the intractability of the game under move-count parametrization and enable efficient solutions under several natural structural restrictions.

0 citationsRead paper

Structured Spectral Graph Learning for Multi-label Abnormality Classification in 3D Chest CT Scans

Oct 12, 2025

Modeling long-range spatial dependencies in multi-label thoracic CT abnormality classification remains challenging due to the computational inefficiency of 3D CNNs and the heavy reliance of Vision Transformers (ViTs) on large-scale pretraining. Method: We propose a lightweight, graph-structured 2.5D approach: modeling CT volumes as weighted graphs whose nodes are axial slice triplets; employing spectral graph convolution to capture inter-slice long-range dependencies; and designing dedicated node representation learning, edge weighting, and multi-strategy graph aggregation mechanisms—eliminating the need for 3D convolutions or domain-specific large-scale pretraining. Contribution/Results: Our method achieves competitive classification performance with mainstream visual encoders across three independent multi-institutional datasets, significantly improving cross-center generalization. It further demonstrates strong transferability to radiology report generation and abdominal CT analysis, validating its effectiveness, robustness, and scalability.

0 citationsRead paper

Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans

Aug 01, 2025

Addressing the challenges of modeling long-range spatial dependencies, high computational cost, and reliance on large-scale in-domain pretraining in multi-label abnormality classification from 3D chest CT volumes, this paper proposes a graph-structured spectral-domain learning method. We represent the CT volume as a structured graph, with axial slice triplets serving as nodes, and employ spectral graph convolution to efficiently capture global spatial relationships. By integrating anatomical priors, our approach overcomes the limited receptive field of 3D convolutions and the prohibitive computational overhead of Vision Transformers (ViTs), while eliminating the need for large-scale in-domain pretraining. Experiments demonstrate competitive performance on multi-label classification, strong cross-dataset generalization, and robustness to z-axis translation. Ablation studies validate the effectiveness of both the graph construction strategy and the spectral convolution design.

0 citationsRead paper