2022
DOI: 10.1038/s41598-022-06718-2
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Automated human cell classification in sparse datasets using few-shot learning

Abstract: Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techniques. In practice, a large amount of data is required to accurately train these deep learning models. However, due to the sparse human cell datasets currently available, the performance of these models is typically low. This study investigates the feasibility of us… Show more

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Cited by 19 publications
(5 citation statements)
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“…The usage of association rules mining allowed us to answer these questions. Applying this and other data mining methods in education remains relevant and has enough use-cases ( Villegas-Ch et al 2021 ; Walsh et al 2022 ).…”
Section: Resultsmentioning
confidence: 99%
“…The usage of association rules mining allowed us to answer these questions. Applying this and other data mining methods in education remains relevant and has enough use-cases ( Villegas-Ch et al 2021 ; Walsh et al 2022 ).…”
Section: Resultsmentioning
confidence: 99%
“…In medical image processing, due to the difficulty of biopsy label acquisition, Qinghua et al first attempted to introduce the few-shot learning into the ultrasound breast tumor diagnosis system [31] and achieved excellent performance. In recent years, the few-shot method has been widely used in the medical field, including the recognition of COVID-19 from rare chest images [32], human cell categorization in rare datasets [33], autism facial feature categorization [34], skin image categorization [35], and healthcare safety monitoring [36].…”
Section: B the Development Of Few/zero-shot Learning In Different Fieldsmentioning
confidence: 99%
“…The experiments presented below are clearly beyond human training capacity, but nevertheless represent a very good set of preliminary stress tests to provide support for all our claims, from Critical Encoding to Memory Robustness. The demonstration that back propagation step is not generally required for very good performance of continues learning as well as small footprint of nodes and input data involved in memory formation are representative of human few-shot learning [66][67][68][69] as well as relevant to the larger issues introduced in our paper.…”
mentioning
confidence: 90%