43 citations · 107 across the 13 of their papers we have counts for
5 papers · 1 filter
Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation
Vemund Fredriksen, Svein Ole M. Svele, André Pedersen +3
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutiona…
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…
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
David Bouget, André Pedersen, Johanna Vanel +2
As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strat…
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey +2
Meningiomas are the most common type of primary brain tumor, accounting for approximately 30% of all brain tumors. A substantial number of these tumors are never surgically removed…