activity
20242026
collaborators

9 papers

cs.CV2026

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…

cs.CV2025

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…

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

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…

cs.CV2025

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…

cs.CV2025

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…