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Two-Staged Self-Attention Based Neural Model for Lung Cancer Recognition

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dc.contributor.author Samarin A.
dc.contributor.author Savelev A.
dc.contributor.author Malykh V.
dc.date.accessioned 2021-02-25T06:55:18Z
dc.date.available 2021-02-25T06:55:18Z
dc.date.issued 2020
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161527
dc.description.abstract © 2020 IEEE. Our work is devoted to the neoplasms presence recognition problem in the context of lung computer tomography photographs analysis. This problem is urgent due to the high lung cancer mortality rate. We propose a monochrome lungs tomography photographs analysis engine which could be useful for online medical consultation services. Our approach uses two-staged a self-attention based architecture and demonstrates results of 0.99F1 score. The presented results are obtained on open dataset of 10052 images.
dc.title Two-Staged Self-Attention Based Neural Model for Lung Cancer Recognition
dc.type Conference Paper
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 50
dc.source.id SCOPUS-2020-SID85099576925


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  • Публикации сотрудников КФУ Scopus [22633]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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