390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Erasmus MCNL13 papers
- Eindhoven University of TechnologyNL5 papers
- Erasmus University RotterdamNL4 papers
- Amsterdam University Medical CentersNL3 papers
- University Medical Center GroningenNL3 papers
- University of AmsterdamNL3 papers
- Leeds Teaching Hospitals NHS TrustGB2 papers
- Technical University of MunichDE2 papers
- The University of Texas Southwestern Medical CenterUS2 papers
- University of StavangerNO2 papers
- Ain Shams UniversityEG1 paper
- Alberta Machine Intelligence InstituteCA1 paper
17 papers
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl +3
Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi-task Deep Learning framework…
Treatment effect estimation by comparing observed and predicted outcomes: conditions for valid inference and practical illustration
Lotta M. Meijerink, Artuur M. Leeuwenberg, Jungyeon Choi +6
Prediction models developed before the introduction of a new treatment may be used to estimate treatment effects of newly introduced treatments. One approach, known as model-based…
The 4D Human Embryonic Brain Atlas: spatiotemporal atlas generation for rapid anatomical changes
Wietske A. P. Bastiaansen, Melek Rousian, Anton H. J. Koning +4
Early brain development is crucial for lifelong neurodevelopmental health. However, current clinical practice offers limited knowledge of normal embryonic brain anatomy on ultrasou…
Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them
S. A. N. Nouwens, B. de Jager, M. M. Paulides +1
Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for c…
Accelerating soft-constrained MPC for linear systems through online constraint removal
S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels
Optimization-based controllers, such as Model Predictive Control (MPC), have attracted significant research interest due to their intuitive concept, constraint handling capabilitie…
Approximate Kalman filtering for large-scale systems with an application to hyperthermia cancer treatments
S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels
Accurate state estimates are required for increasingly complex systems, to enable, for example, feedback control. However, available state estimation schemes are not necessarily re…
Constraint Removal for MPC with Performance Preservation and a Hyperthermia Cancer Treatment Case Study
S. A. N. Nouwens, B. de Jager, M. M. Paulides +1
Model predictive control (MPC) is an optimization-based control strategy with broad industrial adoption. Unfortunately, the required computation time to solve the receding-horizon…
AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan +13
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an over…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez +7
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated pro…
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp +9
Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's cl…
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation
Neel Kanwal, Trygve Eftestol, Farbod Khoraminia +2
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Ima…
Constraint-Adaptive MPC for linear systems: A system-theoretic framework for speeding up MPC through online constraint removal
S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels
Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online c…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos +10
Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to it…
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices
Roya Norouzi Kandalan, Dan Nguyen, Nima Hassan Rezaeian +5
This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adap…
The 'nanobig rods' class of gold nanorods: optimized dimensions for improved in vivo therapeutic and imaging efficacy
Constantin Ungureanu, Gerben A. Koning, Ton G. van Leeuwen +1
Currently, gold nanorods can be synthesized in a wide range of sizes. However, for intended biological applications gold nanorods with approximate dimensions 50 nm x 15 nm are used…