Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features
Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D. M. L. D., & Silva, C. A. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319032 · Cited by 108 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · see citing works
“…(* p < 5 · 10 − 2 , ** p < 10 − 3 ). including the semi-automatic system of Bakas et al. (2015) (bakas1) who won the latest challenge and the method of Pereira et al. (2015) (peres1), which is based on grade-specific 2D CNNs and requires visual inspection of the tumor and identification of the…”
BackgroundMethodInfluentialBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813 · see citing works
Brain tumor detection using fusion of hand crafted and deep learning features
Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.09.007 · Cited by 433 · see citing works
- Authors
- Pereira, S., Pinto, A., Alves, V., & Silva, C. A.
- Affiliations
- University of Minho (PT)
- Corresponding author
- Pereira, Sérgio
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 131-143
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2474982400
- Cited by
- 135
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 10 · 2023: 9 · 2022: 17 · 2021: 13 · 2020: 21 · 2019: 17 · 2018: 19 · 2017: 16 · 2016: 9
- FWCI
- 22.52
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (unspecified)
- Locations
- Lecture notes in computer science (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- 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 › Genetics › Glioma Diagnosis and Treatment
- Keywords
- glioma segmentation, brain tumor segmentation, deep convolutional neural networks, multimodal MRI, high-grade glioma, low-grade glioma, BRATS 2015, dropout, overfitting
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 ›
Are you an author, or do you see a problem with this page? Request a review
