1 citations · 1 across the 7 of their papers we have counts for
10 papers
Evaluating the Effects of Inter-Observer and Model Variability on Radiological Peritoneal Cancer Index Assessment
Savvas Saragiotis, Pieter C. Gort, Lotte J. S. Fleurkens-Ewals +5
Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent improvements in these metrics trans…
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers +1
Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints. Traditional sampling theory s…
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…
Deep Learning-Based Segmentation of Peritoneal Cancer Index Regions from CT Imaging
Pieter C. Gort, Lotte J. S. Fleurkens-Ewals, Lenah D. Kampmeijer +8
Peritoneal metastases (PM) are staged using the surgically determined Peritoneal Cancer Index (sPCI), which requires invasive laparoscopic assessment. Although CT is routinely used…
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…
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer Vision
Tim J. M. Jaspers, Ronald L. P. D. de Jong, Yasmina Al Khalil +9
Over the past decade, computer vision applications in minimally invasive surgery have rapidly increased. Despite this growth, the impact of surgical computer vision remains limited…