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BraTS 2014
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EmailClick to copy linkLink copiedCiteTechnical University of Munich, BraTS 2014 [Dataset]. https://opendatalab.com/OpenDataLab/BraTS_2014zipAvailable download formatsDataset provided byMassachusetts General Hospital
Technical University of Munich
National Institutes of Health, USA
University of BernDescriptionBRATS 2014 is a brain tumor segmentation dataset.
Sensitivity and specificity of different algorithms for ten-fold...
- plos.figshare.com
- figshare.com
xlsUpdated Jun 9, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Sensitivity and specificity of different algorithms for ten-fold cross-validation. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t005xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t005Dataset updatedJun 9, 2023AuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionSensitivity and specificity of different algorithms for ten-fold cross-validation.
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Displacement vector for multispectral co-occurrence matrices.
- plos.figshare.com
xlsUpdated Jun 9, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Displacement vector for multispectral co-occurrence matrices. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t007xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t007Dataset updatedJun 9, 2023Dataset provided byPLOS ONEAuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionDisplacement vector for multispectral co-occurrence matrices.
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Accuracy and AUC of different algorithms for ten-fold cross-validation.
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Accuracy and AUC of different algorithms for ten-fold cross-validation. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t004Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAccuracy and AUC of different algorithms for ten-fold cross-validation.
Textural features obtained from individual subband co-occurrence and...
- plos.figshare.com
xlsUpdated Jun 6, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Textural features obtained from individual subband co-occurrence and multispectral co-occurrence for brain images with high and low grade tumors. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t002Dataset updatedJun 6, 2023AuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionTextural features obtained from individual subband co-occurrence and multispectral co-occurrence for brain images with high and low grade tumors.
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Textural features obtained from individual subband co-occurrence and...
- plos.figshare.com
xlsUpdated Jun 9, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Textural features obtained from individual subband co-occurrence and multispectral co-occurrence for two example images. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t001xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t001Dataset updatedJun 9, 2023Dataset provided byPLOS ONEAuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionTextural features obtained from individual subband co-occurrence and multispectral co-occurrence for two example images.
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Performance analysis of different algorithms using leave-one-out...
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteShaswati Roy; Pradipta Maji (2023). Performance analysis of different algorithms using leave-one-out cross-validation. [Dataset]. http://doi.org/10.1371/journal.pone.0250964.t003xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0250964.t003Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsShaswati Roy; Pradipta MajiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionPerformance analysis of different algorithms using leave-one-out cross-validation.
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National Institutes of Health, USA
University of Bern
BRATS 2014 is a brain tumor segmentation dataset.