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Medical University of Graz

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Selected work

Representative Papers

Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs

Jul 28, 2026

This work addresses the challenges of modeling complex temporal dynamics and patient heterogeneity in longitudinal clinical data by proposing a temporal graph-based contrastive graph neural network approach. The method constructs multivariate disease trajectories as temporal graphs and incorporates structure-aware random walks to guide contrastive learning, effectively preserving both temporal context and trajectory topology. By uniquely integrating structure-aware random walks with contrastive learning, the framework learns expressive embeddings of patient observation nodes, significantly enhancing robust clustering of patients with similar disease progression patterns and uncovering latent evolutionary structures inherent in longitudinal clinical data.

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Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation

May 15, 2026

Standard CNNs for whole-heart multi-chamber CT segmentation often lack explicit anatomical constraints, compromising clinical reliability. This work proposes a lightweight approach that explicitly incorporates statistical shape priors through a shape-aware loss and a 3D U-Net variant guided by spatial label distribution heatmaps. The method is systematically evaluated on the MM-WHS CT and WHS++ datasets. Results reveal that, despite modern architectures implicitly learning substantial anatomical regularities from data, explicitly integrating handcrafted shape priors yields only marginal and inconsistent performance gains—and frequently leads to degradation. These findings underscore the limited added value of manually designed anatomical priors when deployed within highly data-driven deep learning models.

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Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation

Mar 05, 2026

This work proposes TopKGraphs, a non-parametric and interpretable method for robust node similarity estimation in sparse, noisy, or heterogeneous networks. By anchoring random walks at source nodes and guiding transitions via Jaccard similarity, the approach treats walks as local neighborhood samplers. It generates partial node rankings and constructs an affinity matrix through robust rank aggregation, effectively integrating local structural cues with global walk-based information. Notably, TopKGraphs operates without relying on stationary distributions or learned embeddings. Extensive evaluations demonstrate its superior performance over established baselines—including Jaccard, Dice, personalized PageRank, and Node2Vec—across synthetic graphs, k-nearest neighbor graphs, and protein–protein interaction networks, highlighting its accuracy and robustness under diverse and challenging conditions.

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Recent publications

Latest Papers

Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs

Jul 28, 2026

This work addresses the challenges of modeling complex temporal dynamics and patient heterogeneity in longitudinal clinical data by proposing a temporal graph-based contrastive graph neural network approach. The method constructs multivariate disease trajectories as temporal graphs and incorporates structure-aware random walks to guide contrastive learning, effectively preserving both temporal context and trajectory topology. By uniquely integrating structure-aware random walks with contrastive learning, the framework learns expressive embeddings of patient observation nodes, significantly enhancing robust clustering of patients with similar disease progression patterns and uncovering latent evolutionary structures inherent in longitudinal clinical data.

0 citationsRead paper

Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation

May 15, 2026

Standard CNNs for whole-heart multi-chamber CT segmentation often lack explicit anatomical constraints, compromising clinical reliability. This work proposes a lightweight approach that explicitly incorporates statistical shape priors through a shape-aware loss and a 3D U-Net variant guided by spatial label distribution heatmaps. The method is systematically evaluated on the MM-WHS CT and WHS++ datasets. Results reveal that, despite modern architectures implicitly learning substantial anatomical regularities from data, explicitly integrating handcrafted shape priors yields only marginal and inconsistent performance gains—and frequently leads to degradation. These findings underscore the limited added value of manually designed anatomical priors when deployed within highly data-driven deep learning models.

0 citationsRead paper

Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation

Mar 05, 2026

This work proposes TopKGraphs, a non-parametric and interpretable method for robust node similarity estimation in sparse, noisy, or heterogeneous networks. By anchoring random walks at source nodes and guiding transitions via Jaccard similarity, the approach treats walks as local neighborhood samplers. It generates partial node rankings and constructs an affinity matrix through robust rank aggregation, effectively integrating local structural cues with global walk-based information. Notably, TopKGraphs operates without relying on stationary distributions or learned embeddings. Extensive evaluations demonstrate its superior performance over established baselines—including Jaccard, Dice, personalized PageRank, and Node2Vec—across synthetic graphs, k-nearest neighbor graphs, and protein–protein interaction networks, highlighting its accuracy and robustness under diverse and challenging conditions.

0 citationsRead paper