20 citations · 25 across the 16 of their papers we have counts for
16 papers
Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine
Christiaan G. A. Viviers, Koen de Bruin, Mirre M. Trines +6
Nanomedicine research spans delivery chemistry, immunology, imaging, biomaterials, and disease-specific translational science, yet its conceptual design space remains fragmented ac…
Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers
Cris Claessens, Christiaan Viviers, Giacomo D'Amicantonio +2
We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extrac…
MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation
Francisco Caetano, Christiaan Viviers, Peter H. N. De With +1
Synthetic medical data offers a scalable solution for training robust models, but significant domain gaps limit its generalizability to real-world clinical settings. This paper add…
Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
Lemar Abdi, Francisco Caetano, Amaan Valiuddin +3
In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contras…
Zero-Shot Image Anomaly Detection Using Generative Foundation Models
Lemar Abdi, Amaan Valiuddin, Francisco Caetano +2
Detecting out-of-distribution (OOD) inputs is pivotal for deploying safe vision systems in open-world environments. We revisit diffusion models, not as generators, but as universal…
MedSymmFlow: Bridging Generative Modeling and Classification in Medical Imaging through Symmetrical Flow Matching
Francisco Caetano, Lemar Abdi, Christiaan Viviers +2
Reliable medical image classification requires accurate predictions and well-calibrated uncertainty estimates, especially in high-stakes clinical settings. This work presents MedSy…
Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models
Francisco Caetano, Christiaan Viviers, Peter H. N. De With +1
Flow Matching has emerged as a powerful framework for learning continuous transformations between distributions, enabling high-fidelity generative modeling. This work introduces Sy…
AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs
Francisco Caetano, Christiaan Viviers, Lena Filatova +2
Ensuring the quality and integrity of medical images is crucial for maintaining diagnostic accuracy in deep learning-based Computer-Aided Diagnosis and Computer-Aided Detection (CA…
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
Francisco Caetano, Christiaan Viviers, Luis A. Zavala-Mondragón +2
Out-of-distribution (OOD) detection holds significant importance across many applications. While semantic and domain-shift OOD problems are well-studied, this work focuses on covar…
Can Your Generative Model Detect Out-of-Distribution Covariate Shift?
Christiaan Viviers, Amaan Valiuddin, Francisco Caetano +4
Detecting Out-of-Distribution (OOD) sensory data and covariate distribution shift aims to identify new test examples with different high-level image statistics to the captured, nor…
Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries
Christiaan G. A. Viviers, Lena Filatova, Maurice Termeer +2
Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing de…
Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma
Christiaan Viviers, Mark Ramaekers, Amaan Valiuddin +9
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options. This research proposes a workflow and deep learning-based segmentation models…
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun +2
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausibl…
Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray
Christiaan G. A. Viviers, Joel de Bruijn, Lena Filatova +2
Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based o…
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun +2
Melanoma is a serious form of skin cancer with high mortality rate at later stages. Fortunately, when detected early, the prognosis of melanoma is promising and malignant melanoma…
Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
Christiaan G. A. Viviers, Mark Ramaekers, Peter H. N. de With +4
Pancreatic cancer is one of the global leading causes of cancer-related deaths. Despite the success of Deep Learning in computer-aided diagnosis and detection (CAD) methods, little…