20 citations · 25 across the 16 of their papers we have counts for
8 papers · 1 filter
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…