H11-M05_WEIGHT.zip
From the dataset abstract
Method: 使用多實例學習訓練WSI的分類與切割模型,只需要給定WSI有無包含腫瘤組織資訊即可訓練具有分割與分類效果的模型。 模型訓練分為兩階段, 第一階段是使用 Self-Supervised Learning 去訓練一個好的 embedder,把patch轉為特徵向量 ,第二階段會使用到訓練好的 Aggregator...
Source: H11-M05_基於不精確標註資料的弱監督式病理影像切割模型
Additional Information
| Field | Value |
|---|---|
| Data last updated | July 10, 2023 |
| Metadata last updated | July 10, 2023 |
| Created | July 10, 2023 |
| Format | application/zip |
| License | Other (Non-Commercial) |
| created | over 2 years ago |
| format | ZIP |
| id | 05553e14-40e9-4ba3-9f79-c2e69aa39486 |
| last modified | over 2 years ago |
| md5 | 555d19a566cba9924f07623470d4edfa |
| mimetype | application/zip |
| on same domain | True |
| package id | a251e0eb-cfe2-487b-8d81-07b2c6767126 |
| position | 1 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/a251e0eb-cfe2-487b-8d81-07b2c6767126/resource/05553e14-40e9-4ba3-9f79-c2e69aa39486/nchcproxy/H11-M05_WEIGHT.zip |
| revision id | f51f2896-3fda-421a-a4b4-cefe8324022d |
| sha256 | 6a09d92043e0a4bd3b634d9c9cf1207573142a6d4348d69d2366b4354c66ed1c |
| size | 58 MiB |
| state | active |
| url type | upload |
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