Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
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粘, 伟. (2024) · Hans Journal of Ophthalmology · doi:10.12677/hjo.2024.134015
Gopal, S. V., & Kavitha, C. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915192
Optimizing Brain Tumor Classification with ViT: A Metaheuristic Approach
Vishnupriya, S., & Sakthisudhan, K. (2025) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-025-02329-9
Karaciğer tümör segmentasyonu için veri füzyonunun ResUNet modeli üzerindeki etkisinin araştırılması
Ertuğrul, Ü. Ş., Kodaz, H., & İnan, O. (2026) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.1708157
- 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
- Publisher
- Springer International Publishing
- 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
- green
- arxiv.org
- Licence
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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