6 citations · 7 across the 7 of their papers we have counts for
7 papers · 1 filter
SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation
Javier Gamazo Tejero, Moritz Schmid, Pablo Márquez Neila +3
This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models lik…
Masked Image Modelling for retinal OCT understanding
Theodoros Pissas, Pablo Márquez-Neila, Sebastian Wolf +2
This work explores the effectiveness of masked image modelling for learning representations of retinal OCT images. To this end, we leverage Masked Autoencoders (MAE), a simple and…
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection
Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi +5
In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting i…
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception
Negin Ghamsarian, Sebastian Wolf, Martin Zinkernagel +2
Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and…
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…
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…
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…