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Zhou et al., 2024 - Google Patents

Growth threshold for pseudo labeling and pseudo label dropout for semi-supervised medical image classification

Zhou et al., 2024

Document ID
13000902028820992795
Author
Zhou S
Tian S
Yu L
Wu W
Zhang D
Peng Z
Zhou Z
Publication year
Publication venue
Engineering Applications of Artificial Intelligence

External Links

Snippet

Semi-supervised learning (SSL) provides methods to improve model performance through unlabeled samples. In medical image analysis, the challenges of multi-category classification and imbalance learning must be addressed effectively. Pseudo labeling is not …
Continue reading at www.sciencedirect.com (other versions)

Classifications

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    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/6269—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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    • G06N3/08—Learning methods
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