Few-shot learning using explainable Siamese twin network for the automated classification of blood c...
Few-shot learning using explainable Siamese twin network for the automated classification of blood cells
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Automated classification of blood cells from microscopic images is an interesting research area owing to advancements of efficient neural network models. The existing deep learning methods rely on large data for network training and generating such large data could be time-consuming. Further, explainability is required via class activation mapping...
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Full title
Few-shot learning using explainable Siamese twin network for the automated classification of blood cells
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TN_cdi_proquest_miscellaneous_3153156148
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_3153156148
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ISSN
0140-0118
E-ISSN
1741-0444
DOI
10.1007/s11517-023-02804-3
