137 citations · 694 across the 38 of their papers we have counts for
31 papers · 1 filter
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…
Correlation-aware active learning for surgery video segmentation
Fei Wu, Pablo Marquez-Neila, Mingyi Zheng +2
Semantic segmentation is a complex task that relies heavily on large amounts of annotated image data. However, annotating such data can be time-consuming and resource-intensive, es…
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…
StofNet: Super-resolution Time of Flight Network
Christopher Hahne, Michel Hayoz, Raphael Sznitman
Time of Flight (ToF) is a prevalent depth sensing technology in the fields of robotics, medical imaging, and non-destructive testing. Yet, ToF sensing faces challenges from complex…
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…
Localized Questions in Medical Visual Question Answering
Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman
Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training th…
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…
Logical Implications for Visual Question Answering Consistency
Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman
Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities. H…
Stochastic Segmentation with Conditional Categorical Diffusion Models
Lukas Zbinden, Lars Doorenbos, Theodoros Pissas +3
Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurat…
Consistency-preserving Visual Question Answering in Medical Imaging
Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman
Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question. Recently, VQA systems in medical imaging have ga…
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervise…
A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision
Laurent Lejeune, Raphael Sznitman
The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is e…
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
Max Allan, Jonathan Mcleod, Congcong Wang +21
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…
A Question-Centric Model for Visual Question Answering in Medical Imaging
Minh H. Vu, Tommy Löfstedt, Tufve Nyholm +1
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transpare…
Fused Detection of Retinal Biomarkers in OCT Volumes
Thomas Kurmann, Pablo Márquez-Neila, Siqing Yu +3
Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In…
Deep Multi Label Classification in Affine Subspaces
Thomas Kurmann, Pablo Marquez Neila, Sebastian Wolf +1
Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations f…
Iterative multi-path tracking for video and volume segmentation with sparse point supervision
Laurent Lejeune, Jan Grossrieder, Raphael Sznitman
Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however t…
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Sebastian Bodenstedt, Max Allan, Anthony Agustinos +17
Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware like tracking sys…
Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery
Thomas Kurmann, Pablo Marquez Neila, Xiaofei Du +4
Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognitio…
Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks
Stefanos Apostolopoulos, Sandro De Zanet, Carlos Ciller +2
The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage dis…