1 citations · 2 across the 20 of their papers we have counts for
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Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation
Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans +7
Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.…
Surgical Anatomy Recognition with Context Learning using Foundation Representations
Ronald L. P. D. de Jong, Tim J. M. Jaspers, Raf A. H. Vervoort +9
Accurate recognition of anatomical structures is essential for safe and effective minimally invasive surgery (MIS), yet it remains underexplored in surgical computer vision due to…
Gaussian Process Prior Variational Autoencoder for Endoscopic Videos
Ivan De Boi, Xinxing Shi, Xiaoyu Jiang +5
Endoscopic video analysis is essential for gastrointestinal diagnosis and computer-assisted interventions, but video sequences are routinely degraded by specular reflections, motio…
Development and evaluation of CADe systems in low-prevalence setting: The RARE25 challenge for early detection of Barrett's neoplasia
Tim J. M. Jaspers, Francisco Caetano, Cris H. B. Claessens +8
Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings are rare. Although many CAD…
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…
Learning to Recognize Correctly Completed Procedure Steps in Egocentric Assembly Videos through Spatio-Temporal Modeling
Tim J. Schoonbeek, Shao-Hsuan Hung, Dan Lehman +4
Procedure step recognition (PSR) aims to identify all correctly completed steps and their sequential order in videos of procedural tasks. The existing state-of-the-art models rely…
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…
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…
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
M. M. A. Valiuddin, R. J. G. van Sloun, C. G. A. Viviers +2
Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumpti…
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…
Find the Assembly Mistakes: Error Segmentation for Industrial Applications
Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung +3
Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although…
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…
Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flows
M. M. A. Valiuddin, C. G. A. Viviers, R. J. G. van Sloun +2
Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifyin…