6 papers · 1 filter
Surgical Anatomy Recognition with Context Learning using Foundation Representations
Ronald L. P. D. de Jong, Tim J. M. Jaspers, Raf A. H. Vervoort +9
Accurate recognition of anatomical structures is essential for safe and effective minimally invasive surgery (MIS), yet it remains underexplored in surgical computer vision due to…
Object Tokens as a Bridge Between Segmentation and Visual Question Answering in Robotic Surgery
Yiping Li, Ronald de Jong, Romy van Jaarsveld +5
Visual Question Answering (VQA) in robotic surgery, referred to as surgical VQA, requires high-level understanding of complex surgical scenes and the integration of visual percepti…
Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification
Hassan Keshvarikhojasteh, Marc Aubreville, Christof A. Bertram +2
Machine learning models have become integral to many fields, but their reliability, defined as producing dependable, trustworthy, and domain-consistent predictions, remains a criti…
Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance
Noortje I. P. Schueler, Nathan C. K. Wong, Richard J. Crawley +3
Background: Quantitative stress perfusion cardiovascular magnetic resonance (CMR) is a powerful tool for assessing myocardial ischemia. Motion correction is essential for accurate…
PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting
Hong Liu, Haosen Yang, Evi M. C. Huijben +4
Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, synthesizing histopathology data…
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,…