activity
20222024
most citedImproved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features

2 citations · 3 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2024

Benchmarking Pretrained Attention-based Models for Real-Time Recognition in Robot-Assisted Esophagectomy

Ronald L. P. D. de Jong, Yasmina al Khalil, Tim J. M. Jaspers +7

Esophageal cancer is among the most common types of cancer worldwide. It is traditionally treated using open esophagectomy, but in recent years, robot-assisted minimally invasive e…

cs.CV2024

Find the Assembly Mistakes: Error Segmentation for Industrial Applications

Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung +3

Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although…

cs.CV20231 cited

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…

eess.IV2023

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…

cs.CV2023

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…

eess.IV2023

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…