33 citations · 39 across the 4 of their papers we have counts for
5 papers
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation
Loic Themyr, Clement Rambour, Nicolas Thome +2
Transformers have proved to be very effective for visual recognition tasks. In particular, vision transformers construct compressed global representations through self-attention an…
Memory transformers for full context and high-resolution 3D Medical Segmentation
Loic Themyr, Clément Rambour, Nicolas Thome +2
Transformer models achieve state-of-the-art results for image segmentation. However, achieving long-range attention, necessary to capture global context, with high-resolution 3D im…
Surgical Data Science -- from Concepts toward Clinical Translation
Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya +47
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a…
Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image
David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro +2
We present Deep Shape-from-Template (DeepSfT), a novel Deep Neural Network (DNN) method for solving real-time automatic registration and 3D reconstruction of a deformable object vi…
SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes
Nader Mahmoud, Alexandre Hostettler, Toby Collins +3
Recovering surgical scene structure in laparoscope surgery is crucial step for surgical guidance and augmented reality applications. In this paper, a quasi dense reconstruction alg…