Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder
摘要 与前列腺癌和乳腺癌等常见癌症不同，罕见癌症的肿瘤分级很困难，而且很大程度上是不确定的，因为样本量小，承担这样的任务需要大量的时间和经验，而且提取人类观察到的模式本身就很困难。最具挑战性的例子之一是肝内胆管癌(ICC)，这是一种起源于胆道系统的原发性肝癌，其肿瘤异质性是公认的，并且没有分级范例或预后生物标志物。本文提出了一种新的基于无监督深卷积自动编码器的聚类模型，该模型基于视觉相似性将246
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相关文章
- 1. Deep Unsupervised Clustering Using Mixture of Autoencoders(2017)
- 2. Deep Unsupervised Clustering Using Mixture of Autoencoders(MIXAE)
- 3. [解读] Deep Clustering for Unsupervised Learning of Visual Features
- 4. Deep Temporal Clustering: Fully unsupervised learning of time-domain features
- 5. Deep Clustering Algorithms
- 6. Deep Clustering with Convolutional Autoencoders
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- 8. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- 9. 论文笔记：Unsupervised Deep Embedding for Clustering Analysis
- 10. Unsupervised Deep Embedding for Clustering Analysis论文介绍