A review of deep learning for brain tumor analysis in MRI
Athinoula A. Martinos Center for Biomedical Imaging · University of Colorado Anschutz Medical Campus · +1 more institution
Abstract
Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and…
Citation impact
- FWCI
- 65.82
- Percentile
- 100%
- References
- 125
Authors
5- FJFelix J. DorfnerCorresponding
Athinoula A. Martinos Center for Biomedical Imaging
- JPJay Patel
Athinoula A. Martinos Center for Biomedical Imaging
- JKJayashree Kalpathy-Cramer
University of Colorado Anschutz Medical Campus
- ERElizabeth R. Gerstner
Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging
- CPChristopher P. Bridge
Athinoula A. Martinos Center for Biomedical Imaging
Topics & keywords
- Transformative learning
- Deep learning
- Magnetic resonance imaging
- Brain tumor
- Segmentation
- Personalized medicine
- Computer science
- Artificial intelligence