7 papers
Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation
Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans +7
Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.…
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…
Gaussian Process Prior Variational Autoencoder for Endoscopic Videos
Ivan De Boi, Xinxing Shi, Xiaoyu Jiang +5
Endoscopic video analysis is essential for gastrointestinal diagnosis and computer-assisted interventions, but video sequences are routinely degraded by specular reflections, motio…
Development and evaluation of CADe systems in low-prevalence setting: The RARE25 challenge for early detection of Barrett's neoplasia
Tim J. M. Jaspers, Francisco Caetano, Cris H. B. Claessens +8
Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings are rare. Although many CAD…
SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition
Yiping Li, Ronald de Jong, Sahar Nasirihaghighi +8
Accurate surgical phase recognition is crucial for computer-assisted interventions and surgical video analysis. Annotating long surgical videos is labor-intensive, driving research…
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,…