Peikari et al., 2018 - Google Patents
A cluster-then-label semi-supervised learning approach for pathology image classificationPeikari et al., 2018
View HTML- Document ID
- 12717100355966128337
- Author
- Peikari M
- Salama S
- Nofech-Mozes S
- Martel A
- Publication year
- Publication venue
- Scientific reports
External Links
Snippet
Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the …
- 238000000034 method 0 abstract description 36
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
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- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
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