Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Journal: Journal of magnetic resonance imaging : JMRI

Published Date: Jan 10, 2025

Abstract

BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.

Authors

Jon André Ottesen

Computational Radiology & Artificial Intelligence (CRAI) Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. jon.a.ottesen@gmail.com.
Elizabeth Tong

Department of Radiology, Stanford University, Stanford, California, USA.
Kyrre Eeg Emblem

Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway.
Anna Latysheva

Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
Greg Zaharchuk

Stanford University, Stanford CA 94305, USA.
Atle Bjornerud
Endre Grøvik

Department of Radiology, Stanford University, Stanford, California, USA.

Keywords

Algorithms Brain Brain Neoplasms Deep Learning Female Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Reproducibility of Results Retrospective Studies Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (39792624)

Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Abstract

Authors

Keywords

External Resources

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