773 citations · 785 across the 10 of their papers we have counts for
10 papers
Lightweight image segmentation for echocardiography
Anders Kjelsrud, Lasse Løvstakken, Erik Smistad +2
Accurate segmentation of the left ventricle in echocardiography can enable fully automatic extraction of clinical measurements such as volumes and ejection fraction. While models c…
Generative augmentations for improved cardiac ultrasound segmentation using diffusion models
Gilles Van De Vyver, Aksel Try Lenz, Erik Smistad +5
One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions…
Regional quality estimation for echocardiography using deep learning
Gilles Van De Vyver, Svein-Erik Måsøy, Håvard Dalen +7
Automatic estimation of cardiac ultrasound image quality can be beneficial for guiding operators and ensuring the accuracy of clinical measurements. Previous work often fails to di…
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
Gilles Van De Vyver, Sarina Thomas, Guy Ben-Yosef +4
Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the curr…
Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images
André Pedersen, Erik Smistad, Tor V. Rise +6
Histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic m…
Code-free development and deployment of deep segmentation models for digital pathology
Henrik Sahlin Pettersen, Ilya Belevich, Elin Synnøve Røyset +5
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the abi…