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Technological University Dublin

Academic institutioneurope · ie
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Research library20linked papers
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Selected work

Representative Papers

Concept-based explanation of gene expression prediction from H&E images

Aug 17, 2026

This study addresses the limitation of existing Vision Transformer interpretability methods in elucidating how morphological concepts contribute to spatial transcriptomics predictions. We propose a concept graph framework integrating Layer-wise Relevance Propagation with Top-K Sparse Autoencoders to enable global morphology-molecular association analysis from H&E images to gene expression, overcoming the constraints of local heatmaps. Experimental results demonstrate that the model achieves an F1 score of 0.872 in iCMS classification, effectively stratifies patient prognosis, and exhibits robust cross-dataset generalizability. Consequently, this work establishes a novel interpretable paradigm for understanding the intrinsic mechanisms linking tissue morphology to transcriptional programs, providing critical insights into the molecular underpinnings of histopathological features.

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From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

Aug 05, 2026

Although existing machine learning models effectively predict outcomes in ischemic stroke, their reliance on continuous variables often conflicts with categorical thresholds recommended in clinical guidelines, limiting real-world applicability. This study systematically evaluates, for the first time, a strategy that replaces continuous predictors with guideline-based categorical encodings. Using gradient boosting models applied to a multicenter European stroke cohort, we compared the predictive performance of this categorization approach against standard continuous inputs across three treatment subgroups. Results demonstrate that in two of the three subgroups, the categorized models performed comparably to their continuous counterparts, with no statistically significant differences in predictive accuracy. Moreover, global feature importance rankings remained highly consistent between approaches, supporting the feasibility of using guideline-aligned categorical features to enhance clinical interpretability without compromising predictive performance.

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Native topological readout on qubit hardware: a Fibonacci-chain benchmark of measurement-compilation trade-offs

May 25, 2026

This work systematically compares the performance trade-offs between native topological fusion readout and grouped Pauli measurements for implementing the Fibonacci anyon Hamiltonian on noisy intermediate-scale quantum hardware. Leveraging Floquet evolution and variational quantum eigensolver (VQE) circuits, the study employs a covariance-aware mean squared error metric to assess energy estimation accuracy. It provides the first quantitative characterization of the trade-off between compilation and measurement costs for these two approaches and introduces a general criterion tailored to two-dimensional topological models. The results reveal that fusion readout simultaneously reduces both mean squared error and sampling variance in Floquet circuits, whereas in VQE settings, grouped Pauli measurements yield marginally higher accuracy at the cost of increased variance—thereby establishing clear guidelines for selecting optimal measurement strategies across different computational scenarios.

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Recent publications

Latest Papers

Concept-based explanation of gene expression prediction from H&E images

Aug 17, 2026

This study addresses the limitation of existing Vision Transformer interpretability methods in elucidating how morphological concepts contribute to spatial transcriptomics predictions. We propose a concept graph framework integrating Layer-wise Relevance Propagation with Top-K Sparse Autoencoders to enable global morphology-molecular association analysis from H&E images to gene expression, overcoming the constraints of local heatmaps. Experimental results demonstrate that the model achieves an F1 score of 0.872 in iCMS classification, effectively stratifies patient prognosis, and exhibits robust cross-dataset generalizability. Consequently, this work establishes a novel interpretable paradigm for understanding the intrinsic mechanisms linking tissue morphology to transcriptional programs, providing critical insights into the molecular underpinnings of histopathological features.

0 citationsRead paper

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

Aug 05, 2026

Although existing machine learning models effectively predict outcomes in ischemic stroke, their reliance on continuous variables often conflicts with categorical thresholds recommended in clinical guidelines, limiting real-world applicability. This study systematically evaluates, for the first time, a strategy that replaces continuous predictors with guideline-based categorical encodings. Using gradient boosting models applied to a multicenter European stroke cohort, we compared the predictive performance of this categorization approach against standard continuous inputs across three treatment subgroups. Results demonstrate that in two of the three subgroups, the categorized models performed comparably to their continuous counterparts, with no statistically significant differences in predictive accuracy. Moreover, global feature importance rankings remained highly consistent between approaches, supporting the feasibility of using guideline-aligned categorical features to enhance clinical interpretability without compromising predictive performance.

0 citationsRead paper

Native topological readout on qubit hardware: a Fibonacci-chain benchmark of measurement-compilation trade-offs

May 25, 2026

This work systematically compares the performance trade-offs between native topological fusion readout and grouped Pauli measurements for implementing the Fibonacci anyon Hamiltonian on noisy intermediate-scale quantum hardware. Leveraging Floquet evolution and variational quantum eigensolver (VQE) circuits, the study employs a covariance-aware mean squared error metric to assess energy estimation accuracy. It provides the first quantitative characterization of the trade-off between compilation and measurement costs for these two approaches and introduces a general criterion tailored to two-dimensional topological models. The results reveal that fusion readout simultaneously reduces both mean squared error and sampling variance in Floquet circuits, whereas in VQE settings, grouped Pauli measurements yield marginally higher accuracy at the cost of increased variance—thereby establishing clear guidelines for selecting optimal measurement strategies across different computational scenarios.

0 citationsRead paper