Cited by 95Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks

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Mok, Tony C. W., Chung, Albert C. S.·2019·Lecture Notes in Computer Science

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Crossref · 10.1007/978-3-030-11723-8_7

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  1. [5]5. Menze, B., Jakab, A., Bauer, S., et al.: The multimodal brain tumorimage segmentation benchmark (brats). IEEE transactions on Medical Imaging 34(10), 19932024 (2015)

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Authors
Mok, T. C. W., & Chung, A. C. S.
Affiliations
Hong Kong University of Science and Technology (HK)
Corresponding author
Mok, Tony C. W.
Venue
Lecture Notes in Computer Science
Published
2019
Pages
70-80
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2806051477
Cited by
95
References
15
Citations by year
2026: 2 · 2025: 10 · 2024: 14 · 2023: 18 · 2022: 19 · 2021: 16 · 2020: 8 · 2019: 6 · 2018: 2
FWCI
7.07
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
data augmentation, brain tumor segmentation, generative adversarial networks, magnetic resonance imaging, limited training data, medical image segmentation

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