15 citations · 40 across the 12 of their papers we have counts for
10 papers · 1 filter
Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training
Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila +4
Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In…
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading
Tatiana Fountoukidou, Raphael Sznitman
Recent advances in machine learning models have greatly increased the performance of automated methods in medical image analysis. However, the internal functioning of such models i…
Geometric Ultrasound Localization Microscopy
Christopher Hahne, Raphael Sznitman
Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has ena…
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Alain Jungo, Lars Doorenbos, Tommaso Da Col +4
Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Det…
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
Michel Hayoz, Christopher Hahne, Mathias Gallardo +4
Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the end…
Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns
Javier Gamazo Tejero, Martin S. Zinkernagel, Sebastian Wolf +2
Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of…