H11-M107_CODE.zip
From the dataset abstract
使用特徵解偶和自適應實例歸一 化重構的域一般性分割模型，以最小化語義信息損失來解開特徵，以提升域一般性分割模型的準確度
Source: H11-M107_Domain generalization for pathology image segmentation
Additional Information
| Field | Value |
|---|---|
| Data last updated | October 11, 2023 |
| Metadata last updated | October 11, 2023 |
| Created | October 11, 2023 |
| Format | ZIP |
| License | Other (Non-Commercial) |
| Created | over 1 year ago |
| Media type | application/zip |
| Size | 54,582,643 |
| format | ZIP |
| has views | True |
| id | 64747380-f38f-4602-bebf-6bf14a35f499 |
| last modified | over 1 year ago |
| md5 | 52d6476c7ec33c85d021b8a86647a509 |
| on same domain | True |
| package id | 348e4caf-a11f-48c6-ac91-b2b4d64bf740 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/348e4caf-a11f-48c6-ac91-b2b4d64bf740/resource/64747380-f38f-4602-bebf-6bf14a35f499/nchcproxy/H11-M107_CODE.zip |
| revision id | d4d2b3ca-c26e-4a4c-841c-5d34f356c9c8 |
| sha256 | f9e3a5cf3013eb1df5a60ca70c2a4ae51f2deb5daa0f2fd23945d51aeb251f30 |
| state | active |
| url type | upload |
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