20 citations · 58 across the 13 of their papers we have counts for
14 papers
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…
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories
Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. de With
Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper address…
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…
Homography Estimation in Complex Topological Scenes
Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. De With
Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most a…
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…
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
Hongxu Yang, Caifeng Shan, R. Arthur Bouwman +3
Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large…
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention. Currently, the state-of-the-art segmentation algorithms are based on convo…
Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks
Willem P. Sanberg, Gijs Dubbelman, Peter H. N. de With
Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolut…