387 citations · 417 across the 16 of their papers we have counts for
16 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…
ORCAS - Orthogonal Representation for Compression of distributed Acoustic Sensing
Roman Pavelkin, Luis A. Zavala-Mondragon, Fons van der Sommen
Distributed Acoustic Sensing (DAS) utilizes fiber-optic cables to create virtual sensor arrays that monitor vibrations and strain over long distances. DAS is particularly useful in…
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…
Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries
Christiaan G. A. Viviers, Lena Filatova, Maurice Termeer +2
Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing de…
IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting
Tim J. Schoonbeek, Tim Houben, Hans Onvlee +2
Although action recognition for procedural tasks has received notable attention, it has a fundamental flaw in that no measure of success for actions is provided. This limits the ap…
Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma
Christiaan Viviers, Mark Ramaekers, Amaan Valiuddin +9
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options. This research proposes a workflow and deep learning-based segmentation models…
A signal processing interpretation of noise-reduction convolutional neural networks
Luis A. Zavala-Mondragón, Peter H. N. de With, Fons van der Sommen
Encoding-decoding CNNs play a central role in data-driven noise reduction and can be found within numerous deep-learning algorithms. However, the development of these CNN architect…
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun +2
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausibl…
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
Christiaan G. A. Viviers, Amaan M. M. Valiuddin, Peter H. N. de With +1
Uncertainty quantification in medical images has become an essential addition to segmentation models for practical application in the real world. Although there are valuable develo…
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun +2
Melanoma is a serious form of skin cancer with high mortality rate at later stages. Fortunately, when detected early, the prognosis of melanoma is promising and malignant melanoma…
Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
Christiaan G. A. Viviers, Mark Ramaekers, Peter H. N. de With +4
Pancreatic cancer is one of the global leading causes of cancer-related deaths. Despite the success of Deep Learning in computer-aided diagnosis and detection (CAD) methods, little…
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke +35
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of c…