Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2052-4463
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features ; volume:9 ; number:1 ; day:14 ; month:6 ; year:2022 ; pages:1-9 ; date:12.2022
Scientific data ; 9, Heft 1 (14.6.2022), 1-9, 12.2022
- Creator
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Sayah, Anousheh
Bencheqroun, Camelia
Bhuvaneshwar, Krithika
Belouali, Anas
Bakas, Spyridon
Sako, Chiharu
Davatzikos, Christos
Alaoui, Adil
Madhavan, Subha
Gusev, Yuriy
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41597-022-01415-1
- URN
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urn:nbn:de:101:1-2022083020354018508159
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:38 AM CEST
Data provider
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Associated
- Sayah, Anousheh
- Bencheqroun, Camelia
- Bhuvaneshwar, Krithika
- Belouali, Anas
- Bakas, Spyridon
- Sako, Chiharu
- Davatzikos, Christos
- Alaoui, Adil
- Madhavan, Subha
- Gusev, Yuriy
- SpringerLink (Online service)
Other Objects (12)
Author Correction: Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
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Author Correction: Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
The Curious Layperson: Fine-Grained Image Recognition Without Expert Labels
USING 3D MODELS TO GENERATE LABELS FOR PANOPTIC SEGMENTATION OF INDUSTRIAL SCENES
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks
Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction
Enhanced Domain Adaptation for Foot Ulcer Segmentation Through Mixing Self-Trained Weak Labels
A teacher-guided early-learning method for medical image segmentation from noisy labels
Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation
Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma
Berliner Labels
The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods
Governance by labels
Author Correction: Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
The Curious Layperson: Fine-Grained Image Recognition Without Expert Labels
USING 3D MODELS TO GENERATE LABELS FOR PANOPTIC SEGMENTATION OF INDUSTRIAL SCENES
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks
Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction
Enhanced Domain Adaptation for Foot Ulcer Segmentation Through Mixing Self-Trained Weak Labels
A teacher-guided early-learning method for medical image segmentation from noisy labels
Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation
Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma
Berliner Labels
The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods