116 citations · 126 across the 7 of their papers we have counts for
7 papers
Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder
Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +4
The development of deep learning methods for magnetic resonance spectroscopy (MRS) is often hindered by limited availability of large, high-quality training datasets. While physics…
SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
Sahar Nasirihaghighi, Negin Ghamsarian, Yiping Li +3
Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (F…
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,…
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…
Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
Yiping Li, Romy van Jaarsveld, Ronald de Jong +6
Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer, offering better patient outcomes compared to open surgery and traditional…
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab +6
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic…