Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Posted on 2019-08-21 - 04:25
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network. Our approach is compared to a pre-train approach and random initialization and obtains the best results in terms of Dice Similarity Coefficient metric for the small subsets of the Brain Tumor Segmentation 2018 database (BRATS2018).
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Kuzina, Anna; Egorov, Evgenii; Burnaev, Evgeny (2019). Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems. Frontiers. Collection. https://doi.org/10.3389/fnins.2019.00844
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AUTHORS (3)
AK
Anna Kuzina
EE
Evgenii Egorov
EB
Evgeny Burnaev
CATEGORIES
- Radiology and Organ Imaging
- Decision Making
- Clinical Nursing: Tertiary (Rehabilitative)
- Image Processing
- Autonomic Nervous System
- Cellular Nervous System
- Biological Engineering
- Sensory Systems
- Central Nervous System
- Neuroscience
- Endocrinology
- Artificial Intelligence and Image Processing
- Signal Processing
- Rehabilitation Engineering
- Biomedical Engineering not elsewhere classified
- Stem Cells
- Neurogenetics
- Developmental Biology