activity
20222026
most citedAdvancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

20 citations · 25 across the 16 of their papers we have counts for

collaborators

16 papers

cs.AI2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…