Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2020-09-01
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| シリーズ: | Applied Sciences |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2076-3417/10/18/6427 |
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