1 citations · 2 across the 5 of their papers we have counts for
5 papers
Deep learning-based compression of giga-resolution whole slide images
Maren Høibø, Etienne Gaucher, Ingerid Reinertsen +2
Implementation of digital pathology leads to an increased number of whole slide images (WSIs). The large size of WSIs is challenging. Today, WSIs are compressed with codecs like JP…
Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion
Soroush Oskouei, Marit Valla, André Pedersen +10
Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists with their tasks and ease the workl…
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Maren Høibø, André Pedersen, Vibeke Grotnes Dale +8
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help fin…
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…
FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
André Pedersen, Marit Valla, Anna M. Bofin +3
Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs)…