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      "title": "Brain Image"
            
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      "title": "Medical Image"
            
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  "id": "4474dd3c-fdb0-4749-a163-8ffbced7905c",
            
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  "isopen": true,
            
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  "license_id": "cc-by",
            
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  "license_title": "Creative Commons Attribution",
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  "license_title": "Creative Commons Attribution",
            
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  "license_url": "http://www.opendefinition.org/licenses/cc-by",
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  "license_url": "http://www.opendefinition.org/licenses/cc-by",
            
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  "maintainer": "Data tester",
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  "maintainer": "Data tester",
            
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  "maintainer_email": "medpacslin@gmail.com",
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  "maintainer_email": "medpacslin@gmail.com",
            
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  "metadata_created": "2023-03-28T02:39:48.272191",
            
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  "metadata_modified": "2024-09-30T07:20:14.716355",
            
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  "name": "ihd-ct",
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  "name": "ihd-ct",
            
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  "notes": "## Overview\r\n\r\nThis dataset, featured in the RSNA 
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  "notes": "## Overview\r\n\r\nThis dataset, featured in the RSNA 
            
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Intracranial Hemorrhage Detection challenge on Kaggle, offers a rich 
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Intracranial Hemorrhage Detection challenge on Kaggle, offers a rich 
            
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collection of brain CT images. It is meticulously categorized into 
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collection of brain CT images. It is meticulously categorized into 
            
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seven distinct classes: 'none', 'epidural', 'intraparenchymal', 
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seven distinct classes: 'none', 'epidural', 'intraparenchymal', 
            
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'intraventricular', 'subarachnoid', and 'subdural'. Each category is 
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'intraventricular', 'subarachnoid', and 'subdural'. Each category is 
            
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represented by 1000 DICOM files, providing a balanced and extensive 
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represented by 1000 DICOM files, providing a balanced and extensive 
            
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dataset for analysis and machine learning applications.\r\n\r\n## 
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dataset for analysis and machine learning applications.\r\n\r\n## 
            
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Dataset Details\r\n\r\n__Categories__: None, Epidural, 
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Dataset Details\r\n\r\n__Categories__: None, Epidural, 
            
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Intraparenchymal, Intraventricular, Subarachnoid, 
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Intraparenchymal, Intraventricular, Subarachnoid, 
            
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Subdural\r\n\r\n__Files__: 1000 DICOM files per 
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Subdural\r\n\r\n__Files__: 1000 DICOM files per 
            
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category\r\n\r\n__Total Images__: 6000 DICOM files\r\n\r\n__Source__: 
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category\r\n\r\n__Total Images__: 6000 DICOM files\r\n\r\n__Source__: 
            
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RSNA Intracranial Hemorrhage Detection Challenge on Kaggle 
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RSNA Intracranial Hemorrhage Detection Challenge on Kaggle 
            
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competitions/rsna-intracranial-hemorrhage-detection/data)\r\n\r\n##Use 
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competitions/rsna-intracranial-hemorrhage-detection/data)\r\n\r\n##Use 
            
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Case##\r\n\r\nThis dataset is ideal for researchers and practitioners 
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Case##\r\n\r\nThis dataset is ideal for researchers and practitioners 
            
31
working on medical imaging. It provides a substantial resource for 
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working on medical imaging. It provides a substantial resource for 
            
32
developing and testing algorithms in the detection of various types of 
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developing and testing algorithms in the detection of various types of 
            
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intracranial hemorrhages.",
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intracranial hemorrhages.",
            
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    "created": "2022-05-20T11:47:43.716226",
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    "created": "2022-05-20T11:47:43.716226",
            
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    "description": "",
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    "description": "National Yang Ming Chiao Tung University (NYCU; 
            
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Chinese: \u570b\u7acb\u967d\u660e\u4ea4\u901a\u5927\u5b78) is a public 
            
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research university in Taipei and Hsinchu, Taiwan. It was created in 
            
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2021 through the merger of National Yang-Ming University and National 
            
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Chiao Tung University. At present, there are 19 colleges, 74 
            
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university/college level research centers, and 1 hospital in Yilan.",
            
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