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{
            
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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Qiran Jia",
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  "author": "Qiran Jia",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2310.06420",
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    "https://doi.org/10.48550/arXiv.2310.06420",
            
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    "https://doi.org/10.48550/arXiv.2308.16150"
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    "https://doi.org/10.48550/arXiv.2308.16150"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2304.01472",
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  "defined_in": "https://doi.org/10.48550/arXiv.2304.01472",
            
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  "doi": "10.57702/croiys5z",
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  "doi": "10.57702/croiys5z",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-02",
            
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  "doi_publisher": "TIB",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Hai Shu",
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      "extra_author": "Hai Shu",
            
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      "orcid": ""
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      "description": "",
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      "description": "",
            
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      "name": "brain-tumor-segmentation",
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      "name": "brain-tumor-segmentation",
            
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      "title": "Brain Tumor Segmentation"
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      "title": "Brain Tumor Segmentation"
            
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    },
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      "description": "",
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      "name": "mri",
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      "title": "MRI"
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      "title": "MRI"
            
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      "description": "",
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      "name": "medical-image-analysis",
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      "name": "medical-image-analysis",
            
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      "title": "Medical Image Analysis"
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      "title": "Medical Image Analysis"
            
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    },
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Medical Imaging",
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      "display_name": "Medical Imaging",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "medical-imaging",
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      "name": "medical-imaging",
            
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      "title": "Medical Imaging"
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      "title": "Medical Imaging"
            
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  "landing_page": "https://github.com/JustaTinyDot/BiTr-Unet",
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  "landing_page": "https://github.com/JustaTinyDot/BiTr-Unet",
            
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  "license_title": null,
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  "metadata_created": "2024-12-02T23:32:21.745184",
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  "metadata_created": "2024-12-02T23:32:21.745184",
            
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  "metadata_modified": "2024-12-02T23:32:22.314743",
            
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  "name": "brats2021",
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  "name": "brats2021",
            
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  "notes": "Anomaly detection is the process of identifying atypical 
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  "notes": "Anomaly detection is the process of identifying atypical 
            
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data samples that significantly deviate from the majority of the 
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data samples that significantly deviate from the majority of the 
            
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dataset. In the realm of clinical screening and diagnosis, detecting 
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dataset. In the realm of clinical screening and diagnosis, detecting 
            
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abnormalities in medical images holds great importance.",
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abnormalities in medical images holds great importance.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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      "created": "2024-12-03T00:20:35",
            
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      "data": [
            
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      "description": "The json representation of the dataset with its 
            
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distributions based on DCAT.",
            
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      "format": "JSON",
            
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      "hash": "",
            
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      "id": "34c3088f-7ae9-41db-a279-ccb482eca9cd",
            
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      "last_modified": "2024-12-02T23:32:22.306420",
            
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      "display_name": "BraTS",
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      "display_name": "BraTS",
            
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      "name": "Brain Tumor",
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    },
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    },
            
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    {
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      "name": "Medical Imaging",
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      "display_name": "brain tumors",
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      "display_name": "brain tumors",
            
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      "id": "19fdaaa9-ac0b-4e99-8930-7d95d69c3f8c",
            
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      "name": "brain tumors",
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      "name": "brain tumors",
            
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    }
            
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  "title": "BraTS2021",
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  "title": "BraTS2021",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}