H11-M05_CODE.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 | ZIP |
License | Other (Non-Commercial) |
Created | over 1 year ago |
Media type | application/zip |
Size | 1,401,101 |
format | ZIP |
id | 03fbb81e-bb32-4a54-b936-1b40ea6815e7 |
last modified | over 1 year ago |
md5 | f9a2f013495d83f08b0bc1468a917c2d |
on same domain | True |
package id | a251e0eb-cfe2-487b-8d81-07b2c6767126 |
proxy url | https://scidm.nchc.org.tw/en/dataset/a251e0eb-cfe2-487b-8d81-07b2c6767126/resource/03fbb81e-bb32-4a54-b936-1b40ea6815e7/nchcproxy/H11-M05_CODE.zip |
revision id | bf30a774-796a-4f96-ad48-42220b032858 |
sha256 | 37dbdd6ee6f51610588f327a31fa19675910d45058cd3d65ab307cc8b1168dbc |
state | active |
url type | upload |
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