H11-M06_WEIGHT.zip
From the dataset abstract
Method 使用無監督式學習訓練之病理影像異常檢測模型，僅須提供未帶有腫瘤區域的資料來訓練模型，便可協助醫師偵測出病理影像中可能的腫瘤區域。 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 Image Encoder (類似 MoCo) ，第二階段會使用到訓練好的 Encoder 來替所有圖片提取...
Source: H11-M06_考慮到例外分佈的病理影像標註品質評估技術
Additional Information
| Field | Value |
|---|---|
| Data last updated | July 13, 2023 |
| Metadata last updated | July 13, 2023 |
| Created | July 13, 2023 |
| Format | application/zip |
| License | Other (Non-Commercial) |
| created | over 2 years ago |
| format | ZIP |
| id | adb5ac96-6080-42b2-a9fc-63cd99b21a51 |
| last modified | over 2 years ago |
| md5 | a7ed0625bfb5419a7102d425a6d8dad0 |
| mimetype | application/zip |
| on same domain | True |
| package id | eb4281b3-2ef1-4551-ba3f-11b75dae8461 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/eb4281b3-2ef1-4551-ba3f-11b75dae8461/resource/adb5ac96-6080-42b2-a9fc-63cd99b21a51/nchcproxy/H11-M06_WEIGHT.zip |
| revision id | 79858090-5fc8-41d2-bdda-180dd0940dca |
| sha256 | 241021f29c9c07c09b50a1b16a3f7ce6cfa5b3ef10012a0756efbe3e142f2d0b |
| size | 181.6 MiB |
| state | active |
| url type | upload |
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