10 citations · 20 across the 3 of their papers we have counts for
3 papers
Scaling up self-supervised learning for improved surgical foundation models
Tim J. M. Jaspers, Ronald L. P. D. de Jong, Yiping Li +12
Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on extensive datasets. However,…
Supervised Representation Learning towards Generalizable Assembly State Recognition
Tim J. Schoonbeek, Goutham Balachandran, Hans Onvlee +5
Assembly state recognition facilitates the execution of assembly procedures, offering feedback to enhance efficiency and minimize errors. However, recognizing assembly states poses…
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep co…