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University Medical Center Utrecht

Academic institutioneurope · nl
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Research library15linked papers
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Selected work

Representative Papers

Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective

Sep 11, 2026

Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decisions may depend on specific risk thresholds. We introduce Smooth Net Benefit ($σ$NB), a differentiable approximation of Net Benefit designed to align model training with threshold-specific clinical utility. Materials and Methods We evaluated $σ$NB as a training objective for logistic regression, generalized additive models (GAMs), and XGBoost with three Hessian implementations. Experiments used the Framingham cardiovascular risk dataset and 44 TabZilla datasets comprising 72 dataset-threshold combinations. Results $σ$NB training did not consistently improve Net Benefit in Framingham. Across the TabZilla benchmark, mean standardized Net Benefit for logistic regression increased from 0.5669 with NLL to 0.5765 with $σ$NB (mean difference 0.0096, 95% CI -0.0001 to 0.0193). For GAMs, mean standardized Net Benefit decreased from 0.5921 to 0.5625 (mean difference -0.0296, 95% CI -0.0721 to 0.0129). For XGBoost, NLL achieved 0.6745 compared with 0.6723--0.6735 across $σ$NB implementations. In logistic regression, $σ$NB gains were positively associated with the performance advantage of XGBoost over NLL-trained logistic regression. Discussion The effect of $σ$NB was context dependent, with modest gains concentrated in logistic regression and little benefit for more flexible model classes. This suggests that decision-focused optimization may be most useful when limited model flexibility leaves greater scope for improvement. Conclusion Our results do not support $σ$NB as a general replacement for NLL training, but support further investigation of decision-focused objectives in settings where conventional likelihood-based training may not adequately capture decision-relevant structure.

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Less Annotation, More Interpretation: Prior-Guided Concept Bottleneck Models for Interpretable Cancer Imaging Diagnosis

Aug 13, 2026

This work addresses the challenge of applying concept bottleneck models to cancer imaging diagnosis, where reliance on extensive instance-level concept annotations limits practicality. To mitigate this dependency, the authors propose a prior-guided hybrid concept bottleneck model that integrates sparse concept annotations, class-conditional concept distribution matching on unlabeled data, and prior-informed initialization of the diagnostic head. Evaluated on multi-center cancer imaging datasets, the method demonstrates strong performance even with only 10% concept annotation coverage, achieving concept AUCs of 0.741, 0.787, and 0.642 for breast masses, calcifications, and pulmonary nodules, respectively. The approach attains diagnostic accuracy comparable to black-box models while preserving interpretability through explicit concept reasoning.

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Recent publications

Latest Papers

Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective

Sep 11, 2026

Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decisions may depend on specific risk thresholds. We introduce Smooth Net Benefit ($σ$NB), a differentiable approximation of Net Benefit designed to align model training with threshold-specific clinical utility. Materials and Methods We evaluated $σ$NB as a training objective for logistic regression, generalized additive models (GAMs), and XGBoost with three Hessian implementations. Experiments used the Framingham cardiovascular risk dataset and 44 TabZilla datasets comprising 72 dataset-threshold combinations. Results $σ$NB training did not consistently improve Net Benefit in Framingham. Across the TabZilla benchmark, mean standardized Net Benefit for logistic regression increased from 0.5669 with NLL to 0.5765 with $σ$NB (mean difference 0.0096, 95% CI -0.0001 to 0.0193). For GAMs, mean standardized Net Benefit decreased from 0.5921 to 0.5625 (mean difference -0.0296, 95% CI -0.0721 to 0.0129). For XGBoost, NLL achieved 0.6745 compared with 0.6723--0.6735 across $σ$NB implementations. In logistic regression, $σ$NB gains were positively associated with the performance advantage of XGBoost over NLL-trained logistic regression. Discussion The effect of $σ$NB was context dependent, with modest gains concentrated in logistic regression and little benefit for more flexible model classes. This suggests that decision-focused optimization may be most useful when limited model flexibility leaves greater scope for improvement. Conclusion Our results do not support $σ$NB as a general replacement for NLL training, but support further investigation of decision-focused objectives in settings where conventional likelihood-based training may not adequately capture decision-relevant structure.

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Less Annotation, More Interpretation: Prior-Guided Concept Bottleneck Models for Interpretable Cancer Imaging Diagnosis

Aug 13, 2026

This work addresses the challenge of applying concept bottleneck models to cancer imaging diagnosis, where reliance on extensive instance-level concept annotations limits practicality. To mitigate this dependency, the authors propose a prior-guided hybrid concept bottleneck model that integrates sparse concept annotations, class-conditional concept distribution matching on unlabeled data, and prior-informed initialization of the diagnostic head. Evaluated on multi-center cancer imaging datasets, the method demonstrates strong performance even with only 10% concept annotation coverage, achieving concept AUCs of 0.741, 0.787, and 0.642 for breast masses, calcifications, and pulmonary nodules, respectively. The approach attains diagnostic accuracy comparable to black-box models while preserving interpretability through explicit concept reasoning.

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