Changes

On December 16, 2024 at 7:27:46 PM UTC, admin:

Changed title to Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (previously AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation)

Updated description of Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation from
AMOS is a large-scale abdominal multi-organ benchmark for versatile medical image segmentation.
to
A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Removed the following tags from Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Added the following tags to Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
- Medical Image Segmentation
- Abdominal CT

Changed value of field defined_in to https://doi.org/10.48550/arXiv.2303.06040 in Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Changed value of field extra_authors to [{'extra_author': 'Haotian Bai', 'orcid': ''}, {'extra_author': 'Jie Yang', 'orcid': ''}, {'extra_author': 'Chongjian Ge', 'orcid': ''}, {'extra_author': 'Ye Zhu', 'orcid': ''}, {'extra_author': 'Ruimao Zhang', 'orcid': ''}, {'extra_author': 'Zhen Li', 'orcid': ''}, {'extra_author': 'Lingyan Zhang', 'orcid': ''}, {'extra_author': 'Wanling Ma', 'orcid': ''}, {'extra_author': 'Xiang Wan', 'orcid': ''}] in Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Changed value of field citation to [] in Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Changed value of field landing_page to https://zenodo.org/record/22060646 in Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Deleted resource Original Metadata from Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

              
    
          
          
        
        
            f 1 { f 1 {
            2   "access_rights": "", 2   "access_rights": "",
            3   "author": "Yuanfeng Ji", 3   "author": "Yuanfeng Ji",
            4   "author_email": "", 4   "author_email": "",
            n 5   "citation": [ n 5   "citation": [],
            6     "https://doi.org/10.48550/arXiv.2407.04651" 
            7   ], 
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            n 9   "defined_in": "https://doi.org/10.48550/arXiv.2402.03526", n 7   "defined_in": "https://doi.org/10.48550/arXiv.2303.06040",
            10   "doi": "10.57702/t7mz824t", 8   "doi": "10.57702/t7mz824t",
            11   "doi_date_published": "2024-12-16", 9   "doi_date_published": "2024-12-16",
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            15   "extra_authors": [ 13   "extra_authors": [
            16     { 14     {
            17       "extra_author": "Haotian Bai", 15       "extra_author": "Haotian Bai",
            18       "orcid": "" 16       "orcid": ""
            19     }, 17     },
            20     { 18     {
            n 21       "extra_author": "Chongjian Ge", n 19       "extra_author": "Jie Yang",
            22       "orcid": "" 20       "orcid": ""
            23     }, 21     },
            24     { 22     {
            n 25       "extra_author": "Jie Yang", n 23       "extra_author": "Chongjian Ge",
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            27     }, 25     },
            28     { 26     {
            29       "extra_author": "Ye Zhu", 27       "extra_author": "Ye Zhu",
            30       "orcid": "" 28       "orcid": ""
            31     }, 29     },
            32     { 30     {
            33       "extra_author": "Ruimao Zhang", 31       "extra_author": "Ruimao Zhang",
            34       "orcid": "" 32       "orcid": ""
            35     }, 33     },
            36     { 34     {
            37       "extra_author": "Zhen Li", 35       "extra_author": "Zhen Li",
            38       "orcid": "" 36       "orcid": ""
            39     }, 37     },
            40     { 38     {
            n 41       "extra_author": "Lingyan Zhanng", n 39       "extra_author": "Lingyan Zhang",
            42       "orcid": "" 40       "orcid": ""
            43     }, 41     },
            44     { 42     {
            45       "extra_author": "Wanling Ma", 43       "extra_author": "Wanling Ma",
            46       "orcid": "" 44       "orcid": ""
            47     }, 45     },
            48     { 46     {
            49       "extra_author": "Xiang Wan", 47       "extra_author": "Xiang Wan",
            50       "orcid": "" 48       "orcid": ""
            51     } 49     }
            52   ], 50   ],
            53   "groups": [ 51   "groups": [
            54     { 52     {
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            57       "id": "7c8cc5f1-a9b2-4924-82ec-9e3aa3049a04", 
            58       "image_display_url": "", 
            59       "name": "image-segmentation", 
            60       "title": "Image Segmentation" 
            61     }, 
            62     { 
            63       "description": "", 
            64       "display_name": "Medical Image Analysis", 
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            66       "image_display_url": "", 
            67       "name": "medical-image-analysis", 
            68       "title": "Medical Image Analysis" 
            69     }, 
            70     { 
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            75       "name": "medical-image-segmentation", 57       "name": "medical-image-segmentation",
            76       "title": "Medical Image Segmentation" 58       "title": "Medical Image Segmentation"
            77     } 59     }
            78   ], 60   ],
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            80   "isopen": false, 62   "isopen": false,
            n 81   "landing_page": "https://arxiv.org/abs/2206.09541", n 63   "landing_page": "https://zenodo.org/record/22060646",
            82   "license_title": null, 64   "license_title": null,
            83   "link_orkg": "", 65   "link_orkg": "",
            84   "metadata_created": "2024-12-16T17:38:42.872014", 66   "metadata_created": "2024-12-16T17:38:42.872014",
            n 85   "metadata_modified": "2024-12-16T17:38:43.235882", n 67   "metadata_modified": "2024-12-16T19:27:45.547643",
            86   "name":  68   "name": 
            87 minal-multi-organ-benchmark-for-versatile-medical-image-segmentation", 69 minal-multi-organ-benchmark-for-versatile-medical-image-segmentation",
            n 88   "notes": "AMOS is a large-scale abdominal multi-organ benchmark for  n 70   "notes": "A large-scale abdominal multi-organ benchmark for 
            89 versatile medical image segmentation.", 71 versatile medical image segmentation",
            90   "num_resources": 1, 72   "num_resources": 0,
            91   "num_tags": 5, 73   "num_tags": 2,
            92   "organization": { 74   "organization": {
            93     "approval_status": "approved", 75     "approval_status": "approved",
            94     "created": "2024-11-25T12:11:38.292601", 76     "created": "2024-11-25T12:11:38.292601",
            95     "description": "", 77     "description": "",
            96     "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", 78     "id": "079d46db-32df-4b48-91f3-0a8bc8f69559",
            97     "image_url": "", 79     "image_url": "",
            98     "is_organization": true, 80     "is_organization": true,
            99     "name": "no-organization", 81     "name": "no-organization",
            100     "state": "active", 82     "state": "active",
            101     "title": "No Organization", 83     "title": "No Organization",
            102     "type": "organization" 84     "type": "organization"
            103   }, 85   },
            104   "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", 86   "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559",
            105   "private": false, 87   "private": false,
            106   "relationships_as_object": [], 88   "relationships_as_object": [],
            107   "relationships_as_subject": [], 89   "relationships_as_subject": [],
            n 108   "resources": [ n 90   "resources": [],
            109     { 
            110       "cache_last_updated": null, 
            111       "cache_url": null, 
            112       "created": "2024-12-16T18:25:29", 
            113       "data": [ 
            114         "dcterms:title", 
            115         "dcterms:accessRights", 
            116         "dcterms:creator", 
            117         "dcterms:description", 
            118         "dcterms:issued", 
            119         "dcterms:language", 
            120         "dcterms:identifier", 
            121         "dcat:theme", 
            122         "dcterms:type", 
            123         "dcat:keyword", 
            124         "dcat:landingPage", 
            125         "dcterms:hasVersion", 
            126         "dcterms:format", 
            127         "mls:task", 
            128         "datacite:isDescribedBy" 
            129       ], 
            130       "description": "The json representation of the dataset with its  
            131 distributions based on DCAT.", 
            132       "format": "JSON", 
            133       "hash": "", 
            134       "id": "8941f67d-8b32-460c-b480-5a0f3f30f3ac", 
            135       "last_modified": "2024-12-16T17:38:43.228060", 
            136       "metadata_modified": "2024-12-16T17:38:43.238869", 
            137       "mimetype": "application/json", 
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            139       "name": "Original Metadata", 
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            146 resource/8941f67d-8b32-460c-b480-5a0f3f30f3ac/download/metadata.json", 
            147       "url_type": "upload" 
            148     } 
            149   ], 
            150   "services_used_list": "", 91   "services_used_list": "",
            151   "state": "active", 92   "state": "active",
            152   "tags": [ 93   "tags": [
            153     { 94     {
            n 154       "display_name": "Abdominal Multi-Organ Segmentation", n 95       "display_name": "Abdominal CT",
            155       "id": "1242151f-1e21-4574-8e5b-73c95ea07e69", 96       "id": "01ab76be-4f16-4093-a7f7-ef5245c8eeea",
            156       "name": "Abdominal Multi-Organ Segmentation", 97       "name": "Abdominal CT",
            157       "state": "active", 98       "state": "active",
            158       "vocabulary_id": null 99       "vocabulary_id": null
            159     }, 100     },
            160     { 101     {
            n 161       "display_name": "Medical Image Analysis", n 
            162       "id": "7723629d-5b10-4e0b-9214-7aeb8f5d58cc", 
            163       "name": "Medical Image Analysis", 
            164       "state": "active", 
            165       "vocabulary_id": null 
            166     }, 
            167     { 
            168       "display_name": "abdominal multi-organ", 
            169       "id": "ffae52fb-9c0e-4bd8-8933-352dd49ebcec", 
            170       "name": "abdominal multi-organ", 
            171       "state": "active", 
            172       "vocabulary_id": null 
            173     }, 
            174     { 
            175       "display_name": "benchmark", 
            176       "id": "e3d4984e-822c-4023-a134-9cacabcfc36d", 
            177       "name": "benchmark", 
            178       "state": "active", 
            179       "vocabulary_id": null 
            180     }, 
            181     { 
            182       "display_name": "medical image segmentation", 102       "display_name": "Medical Image Segmentation",
            183       "id": "5f476bd2-3620-438a-a010-6c317a9c5092", 103       "id": "a90cdc4c-84c9-4603-8c0e-8e18a0ff936f",
            184       "name": "medical image segmentation", 104       "name": "Medical Image Segmentation",
            185       "state": "active", 105       "state": "active",
            186       "vocabulary_id": null 106       "vocabulary_id": null
            187     } 107     }
            188   ], 108   ],
            t 189   "title": "AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for  t 109   "title": "Amos: A large-scale abdominal multi-organ benchmark for 
            190 Versatile Medical Image Segmentation", 110 versatile medical image segmentation",
            191   "type": "dataset", 111   "type": "dataset",
            192   "version": "" 112   "version": ""
            193 } 113 }