Search Results for author: Ingerid Reinertsen

Found 10 papers, 8 papers with code

Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

1 code implementation28 Apr 2023 David Bouget, Demah Alsinan, Valeria Gaitan, Ragnhild Holden Helland, André Pedersen, Ole Solheim, Ingerid Reinertsen

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans.

Segmentation Tumor Segmentation

Code-free development and deployment of deep segmentation models for digital pathology

2 code implementations16 Nov 2021 Henrik Sahlin Pettersen, Ilya Belevich, Elin Synnøve Røyset, Erik Smistad, Eija Jokitalo, Ingerid Reinertsen, Ingunn Bakke, André Pedersen

Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions.

Active Learning Segmentation +1

Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms

1 code implementation19 Jan 2021 David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey, Ole Solheim, Ingerid Reinertsen

A larger number of cases with meningiomas below 3ml might also be needed to improve the performance for the smallest tumors.

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

3 code implementations11 Nov 2020 André Pedersen, Marit Valla, Anna M. Bofin, Javier Pérez de Frutos, Ingerid Reinertsen, Erik Smistad

It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results.

Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture

1 code implementation14 Oct 2020 David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey, Johanna Vanel, Ole Solheim, Ingerid Reinertsen

We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net).


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