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in Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning -
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in Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning -
Added resource Original Metadata to Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
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| 2 | "access_rights": "", | 2 | "access_rights": "", | ||
| 3 | "author": "Ruining Deng", | 3 | "author": "Ruining Deng", | ||
| 4 | "author_email": "", | 4 | "author_email": "", | ||
| 5 | "citation": [], | 5 | "citation": [], | ||
| 6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
| 7 | "defined_in": "https://doi.org/10.48550/arXiv.2306.00047", | 7 | "defined_in": "https://doi.org/10.48550/arXiv.2306.00047", | ||
| 8 | "doi": "10.57702/b7adnp8w", | 8 | "doi": "10.57702/b7adnp8w", | ||
| n | 9 | "doi_date_published": null, | n | 9 | "doi_date_published": "2024-12-16", |
| 10 | "doi_publisher": "TIB", | 10 | "doi_publisher": "TIB", | ||
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| 12 | "domain": "https://service.tib.eu/ldmservice", | 12 | "domain": "https://service.tib.eu/ldmservice", | ||
| 13 | "extra_authors": [ | 13 | "extra_authors": [ | ||
| 14 | { | 14 | { | ||
| 15 | "extra_author": "Yanwei Li", | 15 | "extra_author": "Yanwei Li", | ||
| 16 | "orcid": "" | 16 | "orcid": "" | ||
| 17 | }, | 17 | }, | ||
| 18 | { | 18 | { | ||
| 19 | "extra_author": "Peize Li", | 19 | "extra_author": "Peize Li", | ||
| 20 | "orcid": "" | 20 | "orcid": "" | ||
| 21 | }, | 21 | }, | ||
| 22 | { | 22 | { | ||
| 23 | "extra_author": "Jiacheng Wang", | 23 | "extra_author": "Jiacheng Wang", | ||
| 24 | "orcid": "" | 24 | "orcid": "" | ||
| 25 | }, | 25 | }, | ||
| 26 | { | 26 | { | ||
| 27 | "extra_author": "Lucas W. Remedios", | 27 | "extra_author": "Lucas W. Remedios", | ||
| 28 | "orcid": "" | 28 | "orcid": "" | ||
| 29 | }, | 29 | }, | ||
| 30 | { | 30 | { | ||
| 31 | "extra_author": "Saydolimkhon Agzamkhodjaev", | 31 | "extra_author": "Saydolimkhon Agzamkhodjaev", | ||
| 32 | "orcid": "" | 32 | "orcid": "" | ||
| 33 | }, | 33 | }, | ||
| 34 | { | 34 | { | ||
| 35 | "extra_author": "Zuhayr Asad", | 35 | "extra_author": "Zuhayr Asad", | ||
| 36 | "orcid": "" | 36 | "orcid": "" | ||
| 37 | }, | 37 | }, | ||
| 38 | { | 38 | { | ||
| 39 | "extra_author": "Quan Liu", | 39 | "extra_author": "Quan Liu", | ||
| 40 | "orcid": "" | 40 | "orcid": "" | ||
| 41 | }, | 41 | }, | ||
| 42 | { | 42 | { | ||
| 43 | "extra_author": "Can Cui", | 43 | "extra_author": "Can Cui", | ||
| 44 | "orcid": "" | 44 | "orcid": "" | ||
| 45 | }, | 45 | }, | ||
| 46 | { | 46 | { | ||
| 47 | "extra_author": "Yaohong Wang", | 47 | "extra_author": "Yaohong Wang", | ||
| 48 | "orcid": "" | 48 | "orcid": "" | ||
| 49 | }, | 49 | }, | ||
| 50 | { | 50 | { | ||
| 51 | "extra_author": "Yihan Wang", | 51 | "extra_author": "Yihan Wang", | ||
| 52 | "orcid": "" | 52 | "orcid": "" | ||
| 53 | }, | 53 | }, | ||
| 54 | { | 54 | { | ||
| 55 | "extra_author": "Yucheng Tang", | 55 | "extra_author": "Yucheng Tang", | ||
| 56 | "orcid": "" | 56 | "orcid": "" | ||
| 57 | }, | 57 | }, | ||
| 58 | { | 58 | { | ||
| 59 | "extra_author": "Haichun Yang", | 59 | "extra_author": "Haichun Yang", | ||
| 60 | "orcid": "" | 60 | "orcid": "" | ||
| 61 | }, | 61 | }, | ||
| 62 | { | 62 | { | ||
| 63 | "extra_author": "Yuankai Huo", | 63 | "extra_author": "Yuankai Huo", | ||
| 64 | "orcid": "" | 64 | "orcid": "" | ||
| 65 | } | 65 | } | ||
| 66 | ], | 66 | ], | ||
| 67 | "groups": [ | 67 | "groups": [ | ||
| 68 | { | 68 | { | ||
| 69 | "description": "", | 69 | "description": "", | ||
| 70 | "display_name": "Image Segmentation", | 70 | "display_name": "Image Segmentation", | ||
| 71 | "id": "7c8cc5f1-a9b2-4924-82ec-9e3aa3049a04", | 71 | "id": "7c8cc5f1-a9b2-4924-82ec-9e3aa3049a04", | ||
| 72 | "image_display_url": "", | 72 | "image_display_url": "", | ||
| 73 | "name": "image-segmentation", | 73 | "name": "image-segmentation", | ||
| 74 | "title": "Image Segmentation" | 74 | "title": "Image Segmentation" | ||
| 75 | }, | 75 | }, | ||
| 76 | { | 76 | { | ||
| 77 | "description": "", | 77 | "description": "", | ||
| 78 | "display_name": "Pathology", | 78 | "display_name": "Pathology", | ||
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| 80 | "image_display_url": "", | 80 | "image_display_url": "", | ||
| 81 | "name": "pathology", | 81 | "name": "pathology", | ||
| 82 | "title": "Pathology" | 82 | "title": "Pathology" | ||
| 83 | } | 83 | } | ||
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| 85 | "id": "09d8ef27-bca1-4615-a6fd-3143dd97e276", | 85 | "id": "09d8ef27-bca1-4615-a6fd-3143dd97e276", | ||
| 86 | "isopen": false, | 86 | "isopen": false, | ||
| 87 | "landing_page": "https://github.com/hrlblab/MolecularEL", | 87 | "landing_page": "https://github.com/hrlblab/MolecularEL", | ||
| 88 | "license_title": null, | 88 | "license_title": null, | ||
| 89 | "link_orkg": "", | 89 | "link_orkg": "", | ||
| 90 | "metadata_created": "2024-12-16T20:18:50.501953", | 90 | "metadata_created": "2024-12-16T20:18:50.501953", | ||
| n | 91 | "metadata_modified": "2024-12-16T20:18:50.501958", | n | 91 | "metadata_modified": "2024-12-16T20:18:50.870547", |
| 92 | "name": | 92 | "name": | ||
| 93 | ge-segmentation-with-lay-annotators-via-molecular-empowered-learning", | 93 | ge-segmentation-with-lay-annotators-via-molecular-empowered-learning", | ||
| 94 | "notes": "Multi-class cell segmentation in high-resolution | 94 | "notes": "Multi-class cell segmentation in high-resolution | ||
| 95 | Giga-pixel whole slide images (WSI) is critical for various clinical | 95 | Giga-pixel whole slide images (WSI) is critical for various clinical | ||
| 96 | applications. Training such an AI model typically requires | 96 | applications. Training such an AI model typically requires | ||
| 97 | labor-intensive pixel-wise manual annotation from experienced domain | 97 | labor-intensive pixel-wise manual annotation from experienced domain | ||
| 98 | experts (e.g., pathologists). Moreover, such annotation is error-prone | 98 | experts (e.g., pathologists). Moreover, such annotation is error-prone | ||
| 99 | when differentiating fine-grained cell types (e.g., podocyte and | 99 | when differentiating fine-grained cell types (e.g., podocyte and | ||
| 100 | mesangial cells) via the naked human eye.", | 100 | mesangial cells) via the naked human eye.", | ||
| n | 101 | "num_resources": 0, | n | 101 | "num_resources": 1, |
| 102 | "num_tags": 5, | 102 | "num_tags": 5, | ||
| 103 | "organization": { | 103 | "organization": { | ||
| 104 | "approval_status": "approved", | 104 | "approval_status": "approved", | ||
| 105 | "created": "2024-11-25T12:11:38.292601", | 105 | "created": "2024-11-25T12:11:38.292601", | ||
| 106 | "description": "", | 106 | "description": "", | ||
| 107 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 107 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
| 108 | "image_url": "", | 108 | "image_url": "", | ||
| 109 | "is_organization": true, | 109 | "is_organization": true, | ||
| 110 | "name": "no-organization", | 110 | "name": "no-organization", | ||
| 111 | "state": "active", | 111 | "state": "active", | ||
| 112 | "title": "No Organization", | 112 | "title": "No Organization", | ||
| 113 | "type": "organization" | 113 | "type": "organization" | ||
| 114 | }, | 114 | }, | ||
| 115 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 115 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
| 116 | "private": false, | 116 | "private": false, | ||
| 117 | "relationships_as_object": [], | 117 | "relationships_as_object": [], | ||
| 118 | "relationships_as_subject": [], | 118 | "relationships_as_subject": [], | ||
| t | 119 | "resources": [], | t | 119 | "resources": [ |
| 120 | { | ||||
| 121 | "cache_last_updated": null, | ||||
| 122 | "cache_url": null, | ||||
| 123 | "created": "2024-12-16T18:25:45", | ||||
| 124 | "data": [ | ||||
| 125 | "dcterms:title", | ||||
| 126 | "dcterms:accessRights", | ||||
| 127 | "dcterms:creator", | ||||
| 128 | "dcterms:description", | ||||
| 129 | "dcterms:issued", | ||||
| 130 | "dcterms:language", | ||||
| 131 | "dcterms:identifier", | ||||
| 132 | "dcat:theme", | ||||
| 133 | "dcterms:type", | ||||
| 134 | "dcat:keyword", | ||||
| 135 | "dcat:landingPage", | ||||
| 136 | "dcterms:hasVersion", | ||||
| 137 | "dcterms:format", | ||||
| 138 | "mls:task", | ||||
| 139 | "datacite:isDescribedBy" | ||||
| 140 | ], | ||||
| 141 | "description": "The json representation of the dataset with its | ||||
| 142 | distributions based on DCAT.", | ||||
| 143 | "format": "JSON", | ||||
| 144 | "hash": "", | ||||
| 145 | "id": "62d918b9-36ce-4f57-a2f4-ce74884dbd66", | ||||
| 146 | "last_modified": "2024-12-16T20:18:50.862491", | ||||
| 147 | "metadata_modified": "2024-12-16T20:18:50.873344", | ||||
| 148 | "mimetype": "application/json", | ||||
| 149 | "mimetype_inner": null, | ||||
| 150 | "name": "Original Metadata", | ||||
| 151 | "package_id": "09d8ef27-bca1-4615-a6fd-3143dd97e276", | ||||
| 152 | "position": 0, | ||||
| 153 | "resource_type": null, | ||||
| 154 | "size": 1356, | ||||
| 155 | "state": "active", | ||||
| 156 | "url": | ||||
| 157 | resource/62d918b9-36ce-4f57-a2f4-ce74884dbd66/download/metadata.json", | ||||
| 158 | "url_type": "upload" | ||||
| 159 | } | ||||
| 160 | ], | ||||
| 120 | "services_used_list": "", | 161 | "services_used_list": "", | ||
| 121 | "state": "active", | 162 | "state": "active", | ||
| 122 | "tags": [ | 163 | "tags": [ | ||
| 123 | { | 164 | { | ||
| 124 | "display_name": "cell segmentation", | 165 | "display_name": "cell segmentation", | ||
| 125 | "id": "4a6fcfd3-9f17-4e94-9d02-22455a3973a9", | 166 | "id": "4a6fcfd3-9f17-4e94-9d02-22455a3973a9", | ||
| 126 | "name": "cell segmentation", | 167 | "name": "cell segmentation", | ||
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| 128 | "vocabulary_id": null | 169 | "vocabulary_id": null | ||
| 129 | }, | 170 | }, | ||
| 130 | { | 171 | { | ||
| 131 | "display_name": "image annotation", | 172 | "display_name": "image annotation", | ||
| 132 | "id": "6734bcb9-ad78-4289-8816-09b71832ab8e", | 173 | "id": "6734bcb9-ad78-4289-8816-09b71832ab8e", | ||
| 133 | "name": "image annotation", | 174 | "name": "image annotation", | ||
| 134 | "state": "active", | 175 | "state": "active", | ||
| 135 | "vocabulary_id": null | 176 | "vocabulary_id": null | ||
| 136 | }, | 177 | }, | ||
| 137 | { | 178 | { | ||
| 138 | "display_name": "lay annotators", | 179 | "display_name": "lay annotators", | ||
| 139 | "id": "105da576-11a3-4f31-bc28-a5f5d95d2d77", | 180 | "id": "105da576-11a3-4f31-bc28-a5f5d95d2d77", | ||
| 140 | "name": "lay annotators", | 181 | "name": "lay annotators", | ||
| 141 | "state": "active", | 182 | "state": "active", | ||
| 142 | "vocabulary_id": null | 183 | "vocabulary_id": null | ||
| 143 | }, | 184 | }, | ||
| 144 | { | 185 | { | ||
| 145 | "display_name": "molecular-empowered learning", | 186 | "display_name": "molecular-empowered learning", | ||
| 146 | "id": "3ac1843e-91a9-47ca-961e-7d7725414c38", | 187 | "id": "3ac1843e-91a9-47ca-961e-7d7725414c38", | ||
| 147 | "name": "molecular-empowered learning", | 188 | "name": "molecular-empowered learning", | ||
| 148 | "state": "active", | 189 | "state": "active", | ||
| 149 | "vocabulary_id": null | 190 | "vocabulary_id": null | ||
| 150 | }, | 191 | }, | ||
| 151 | { | 192 | { | ||
| 152 | "display_name": "pathology", | 193 | "display_name": "pathology", | ||
| 153 | "id": "13efdc48-f870-4525-8015-79048d6d7694", | 194 | "id": "13efdc48-f870-4525-8015-79048d6d7694", | ||
| 154 | "name": "pathology", | 195 | "name": "pathology", | ||
| 155 | "state": "active", | 196 | "state": "active", | ||
| 156 | "vocabulary_id": null | 197 | "vocabulary_id": null | ||
| 157 | } | 198 | } | ||
| 158 | ], | 199 | ], | ||
| 159 | "title": "Democratizing Pathological Image Segmentation with Lay | 200 | "title": "Democratizing Pathological Image Segmentation with Lay | ||
| 160 | Annotators via Molecular-empowered Learning", | 201 | Annotators via Molecular-empowered Learning", | ||
| 161 | "type": "dataset", | 202 | "type": "dataset", | ||
| 162 | "version": "" | 203 | "version": "" | ||
| 163 | } | 204 | } |