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2024

Article (contribution to journal)

Kula, F., Litvak, N. , & Craig, T. S. (2024). Conceptualizing the Sample Mean: Insights for Computer Engineering Students in the Learning Process. IEEE transactions on education, 1-7. Advance online publication. https://doi.org/10.1109/TE.2024.3376795

Ye, D., Krzhizhanovskaya, V., & Hoekstra, A. G. (2024). Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots. Journal of computational physics, 497, Article 112639. https://doi.org/10.1016/j.jcp.2023.112639

Wiesner, D. , Suk, J. , Dummer, S., Nečasová, T., Ulman, V., Svoboda, D. , & Wolterink, J. M. (2024). Generative modeling of living cells with SO(3)-equivariant implicit neural representations. Medical image analysis, 91, Article 102991. https://doi.org/10.1016/j.media.2023.102991

van Blooijs, D., Blok, S., Huiskamp, G. J. M., Van Eijsden, P. , Meijer, H. G. E., & Leijten, F. S. S. (2024). The effect of propofol on effective brain networks. Clinical neurophysiology, 161(5), 222-230. https://doi.org/10.1016/j.clinph.2024.01.012

Preprint

Vaish, P., Wang, S. , & Strisciuglio, N. (2024). Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification. ArXiv.org. https://doi.org/10.48550/arXiv.2403.01944

Rygiel, P. , Alblas, D. , Brune, C., Yeung, K. K. , & Wolterink, J. M. (2024). Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. ArXiv.org. https://doi.org/10.48550/arXiv.2403.15314

Bredies, K. , Iglesias, J. A., & Walter, D. (2024). On extremal points for some vectorial total variation seminorms. ArXiv.org. https://doi.org/10.48550/arXiv.2404.12831

Chen, H., Dong, G. , Iglesias, J. A., Liu, W., & Xie, Z. (2024). Second-order flows for approaching stationary points of a class of non-convex energies via convex-splitting schemes. ArXiv.org. https://doi.org/10.48550/arXiv.2402.12173

2023

Article (contribution to journal)

Gerats, B. G. A. , Wolterink, J. M. , & Broeders, I. A. M. J. (2023). 3D human pose estimation in multi-view operating room videos using differentiable camera projections. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4), 1197-1205. https://doi.org/10.1080/21681163.2022.2155580

Doorn, N., Hugte, E. J. H. V., Ciptasari, U., Mordelt, A. , Meijer, H. G. E., Schubert, D. , Frega, M., Nadif Kasri, N. , & Putten, M. J. A. M. V. (2023). An in silico and in vitro human neuronal network model reveals cellular mechanisms beyond NaV1.1 underlying Dravet syndrome. Stem cell reports, 18(8), 1686-1700. https://doi.org/10.1016/j.stemcr.2023.06.003

Bredies, K., Carioni, M., Fanzon, S., & Walter, D. (2023). Asymptotic linear convergence of fully-corrective generalized conditional gradient methods. Mathematical programming. Advance online publication. https://doi.org/10.1007/s10107-023-01975-z

Bredies, K. , Iglesias, J. A., & Mercier, G. (2023). Boundedness and Unboundedness in Total Variation Regularization. Applied mathematics and optimization, 88(2), Article 51. https://doi.org/10.1007/s00245-023-10028-y

Xie, X., Wang, W., Wu, H. , & Guo, M. (2023). Data-driven analysis of parametrized acoustic systems in the frequency domain. Applied mathematical modelling, 124, 791-805. https://doi.org/10.1016/j.apm.2023.08.018

Kappe, K. O., Smorenburg, S. P. M., Hoksbergen, A. W. J. , Wolterink, J. M., & Yeung, K. K. (2023). Deep Learning–Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair. Journal of Endovascular Therapy, 30(6), 822-827. https://doi.org/10.1177/15266028221105840

van Veldhuizen, W. A., van der Wel, H., Kuipers, H. Y., Kraeima, J., ten Duis, K. , Wolterink, J. M., de Vries, J. P. P. M. , Schuurmann, R. C. L., & IJpma, F. F. A. (2023). Development of a Statistical Shape Model and Assessment of Anatomical Shape Variations in the Hemipelvis. Journal of Clinical Medicine, 12(11), Article 3767. https://doi.org/10.3390/jcm12113767

Wotte, Y. P. , Dummer, S. , Botteghi, N. , Brune, C. , Stramigioli, S. , & Califano, F. (2023). Discovering efficient periodic behaviors in mechanical systems via neural approximators. Optimal Control Applications and Methods, 44(6), 3052-3079. https://doi.org/10.1002/oca.3025

Carioni, M. , Iglesias, J. A., & Walter, D. (2023). Extremal Points and Sparse Optimization for Generalized Kantorovich–Rubinstein Norms. Foundations of Computational Mathematics. Advance online publication. https://doi.org/10.1007/s10208-023-09634-7

Lentjes, B. , Spek, L., Bosschaert, M. M. , & Kuznetsov, Y. A. (2023). Periodic Center Manifolds for DDEs in the Light of Suns and Stars. Journal of dynamics and differential equations. Advance online publication. https://doi.org/10.1007/s10884-023-10289-9

Lentjes, B., Windmolders, M. , & Kuznetsov, Y. A. (2023). Periodic Center Manifolds for Nonhyperbolic Limit Cycles in ODEs. International Journal of Bifurcation and Chaos, 33(15), Article 2350184. https://doi.org/10.1142/S0218127423501845

van Praagh, G. D., Davidse, M. E. J. , Wolterink, J. M. , & Slart, R. H. J. A. (2023). Quantitative analysis of aortic Na[¹⁸F]F uptake in macrocalcifications and microcalcifications in PET/CT scans. Medical physics. Advance online publication. https://doi.org/10.1002/mp.16787

Liu, L., Ma, R., van Ooijen, P. M. A., Oudkerk, M., Vliegenthart, R. , Veldhuis, R. N. J. , & Brune, C. (2023). The U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Low-Dose CT. Technologies, 11(4), Article 104. https://doi.org/10.3390/technologies11040104

Cicci, L., Fresca, S. , Guo, M., Manzoni, A., & Zunino, P. (2023). Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Computers and Mathematics with Applications, 149, 1-23. https://doi.org/10.1016/j.camwa.2023.08.016

Van Den Noort, F., Manzini, C., Hofsteenge, M. , Sirmacek, B., Van Der Vaart, C. H. , & Slump, C. H. (2023). Unsupervised convolutional autoencoders for 4D transperineal ultrasound classification. Journal of medical imaging, 10(1), Article 014004. https://doi.org/10.1117/1.JMI.10.1.014004

Přibylová, L., Sevcik, J., Eclerova, V., Klimes, P., Brazdil, M. , & Meijer, H. G. E. (2023). Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase-shifts. Network Neuroscience, 8(1), 293-318. Advance online publication. https://doi.org/10.1162/netn_a_00351

Chapter

Suk, J. , Brune, C. , & Wolterink, J. M. (2023). SE(3) Symmetry Lets Graph Neural Networks Learn Arterial Velocity Estimation from Small Datasets. In O. Bernard, P. Clarysse, N. Duchateau, J. Ohayon, & M. Viallon (Eds.), Functional Imaging and Modeling of the Heart: 12th International Conference, FIMH 2023, Lyon, France, June 19–22, 2023, Proceedings (pp. 445-454) https://doi.org/10.1007/978-3-031-35302-4_46

Conference contribution

Wang, S. , Brune, C. , Veldhuis, R. , & Strisciuglio, N. (2023). DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning. In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (pp. 129-138). Article 10350684 (Proceedings IEEE/CVF International Conference on Computer Vision Workshops (ICCVW); Vol. 2023). IEEE. https://doi.org/10.1109/ICCVW60793.2023.00020

Thibeau-Sutre, E. , Wolterink, J. M., Colliot, O., & Burgos, N. (2023). How Can Data Augmentation Improve Attribution Maps for Disease Subtype Explainability? In O. Colliot, & I. Isgum (Eds.), SPIE Medical Imaging 2023: Image Processing Article 1246424 SPIE. https://doi.org/10.1117/12.2653809

Alblas, D., Hofman, M. , Brune, C., Yeung, K. K. , & Wolterink, J. M. (2023). Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression. In O. Bernard, P. Clarysse, N. Duchateau, J. Ohayon, & M. Viallon (Eds.), Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings (pp. 356-365). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13958 LNCS). Springer. https://doi.org/10.1007/978-3-031-35302-4_37

Thibeau-Sutre, E. , Alblas, D., Buurman, S. , Brune, C. , & Wolterink, J. M. (Accepted/In press). Uncertainty-based quality assurance of carotid artery segmentation. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)

Wang, S. , Veldhuis, R. , Brune, C. , & Strisciuglio, N. (2023). What do neural networks learn in image classification? A frequency shortcut perspective. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1433-1442). Article 10378114 (Proceedings IEEE/CVF International Conference on Computer Vision (ICCV); Vol. 2023). IEEE. https://doi.org/10.1109/ICCV51070.2023.00138

Poster

van Susteren, H. (2023). A biophysical model of neuronal dynamics during energy deprivation. Poster session presented at 6th International BonnBrain ³ Conference 2023
, Bonn, North Rhine-Westphalia, Germany.

Preprint

Carioni, M. , & Grande, L. D. (2023). A General Theory for Exact Sparse Representation Recovery in Convex Optimization. ArXiv.org. https://doi.org/10.48550/arXiv.2311.08072

Wang, S. , Veldhuis, R. , Brune, C. , & Strisciuglio, N. (2023). A Survey on the Robustness of Computer Vision Models against Common Corruptions. ArXiv.org. https://doi.org/10.48550/arXiv.2305.06024

Bredies, K., Carioni, M., Holler, M., Korolev, Y., & Schönlieb, C.-B. (2023). A sparse optimization approach to infinite infimal convolution regularization. ArXiv.org. https://doi.org/10.48550/arXiv.2304.08628

Ye, D. , & Guo, M. (2023). Bayesian approach to Gaussian process regression with uncertain inputs. ArXiv.org.

Cristinelli, G. , Iglesias, J. A., & Walter, D. (2023). Conditional gradients for total variation regularization with PDE constraints: a graph cuts approach. ArXiv.org. https://doi.org/10.48550/arXiv.2310.19777

Ye, D., Krzhizhanovskaya, V., & Hoekstra, A. G. (2023). Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots. ArXiv.org.

Davoli, E., Ferreira, R., Fonseca, I. , & Iglesias, J. A. (2023). Dyadic partition-based training schemes for TV/TGV denoising. ArXiv.org. https://doi.org/10.48550/arXiv.2305.07150

Heeringa, T. J. , Spek, L. , Schwenninger, F. , & Brune, C. (2023). Embeddings between Barron spaces with higher order activation functions. https://doi.org/10.48550/arXiv.2305.15839

Heeringa, T. J., Roith, T. , Brune, C., & Burger, M. (2023). Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow. ArXiv.org. https://doi.org/10.48550/arXiv.2312.02671

Lentjes, B. , Spek, L., Bosschaert, M. M. , & Kuznetsov, Y. A. (2023). Periodic Normal Forms for Bifurcations of Limit Cycles in DDEs.

Laso, P., Cerri, S., Sorby-Adams, A., Guo, J., Mateen, F., Goebl, P., Wu, J., Liu, P., Li, H., Young, S. I., Billot, B., Puonti, O., Sze, G., Payabavash, S., DeHavenon, A., Sheth, K. N., Rosen, M. S., Kirsch, J. , Strisciuglio, N., ... Iglesias, J. E. (2023). Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI. ArXiv.org. https://doi.org/10.48550/arXiv.2312.05119

Dummer, S. , Brune, C. , & Strisciuglio, N. (2023). RSA-INR: Riemannian Shape Autoencoding via 4D Implicit Neural Representations. (pp. 1-26). ArXiv.org. https://arxiv.org/abs/2305.12854

Dummer, S. , Strisciuglio, N. , & Brune, C. (2023). Rda-inr: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations. ArXiv.org. https://doi.org/10.48550/arXiv.2305.12854

Suk, J. , Brune, C. , & Wolterink, J. M. (2023). SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets. (pp. 1). ArXiv.org. https://doi.org/10.48550/arXiv.2302.08780

Alblas, D. , Suk, J. M. , Brune, C., Yeung, K. K. , & Wolterink, J. M. (2023). SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks. ArXiv.org.

Botteghi, N. , Califano, F. , Poel, M. , & Brune, C. (2023). Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models.

Carioni, M., Mukherjee, S., Tan, H. Y., & Tang, J. (2023). Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging. ArXiv.org. https://doi.org/10.48550/arXiv.2311.08972

Working paper

Alblas, D., van den Bosch, M., Feigl, Z. , Klomp, L. J., Rottschäfer, V. , Schwenninger, F. , Spek, L. , Weedage, L., & Zeijlemaker, S. (2023). A Joint Data- and Model-Driven Approach Simulating the Behaviour of a Walkalong Glider. Cambridge University Press. https://doi.org/10.33774/miir-2023-p957p

Saitta, S., Carioni, M., Mukherjee, S., Schönlieb, C.-B., & Redaelli, A. (2023). Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. ArXiv.org. https://doi.org/10.48550/arXiv.2302.12835

This publication list has been generated from UT Research Information.