title: Image anomaly detection in preventive fluorographic
metadata.name: dc.contributor.advisor
metadata.value: Ронжин Александр Леонидович
metadata.lang: ru_RU
metadata.name: dc.contributor.author
metadata.value: Ткачева Дарья Алексеевна
metadata.lang: ru_RU
metadata.name: dc.contributor.author
metadata.value: Tkacheva Daria
metadata.lang: en_GB
metadata.name: dc.contributor.editor
metadata.value: Салищев Сергей Игоревич
metadata.lang: ru_RU
metadata.name: dc.contributor.editor
metadata.value: Salishchev Sergei Igorevich
metadata.lang: en_GB
metadata.name: dc.date.accessioned
metadata.value: 2018-07-26T15:26:26Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-07-26T15:26:26Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018
metadata.name: dc.identifier.other
metadata.value: 040129
metadata.lang: en_GB
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/13611
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: В данной работе рассматривается возможность применения генеративного подхода машинного обучения в области поиска аномалий на рентгенограммах. Применение данного подхода оценивалось по двум метрикам: AUC-ROC и AUC-PR. Обе метрики показали хорошие результаты: площадь под ROC-кривой составила 0.92, под PR-кривой 0.94.
metadata.lang: ru_RU
metadata.name: dc.description.abstract
metadata.value: In this paper, we consider the possibility of applying a generative machine learning approach in the search for anomalies on radiographs. Applying this approach was evaluated using two metrics: AUC-ROC and AUC-PR. Both metrics showed good results: the area under the ROC curve was 0.92, under the PR curve of 0.94.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.name: dc.subject
metadata.value: cвeртoчный aвтoэнкoдeр
metadata.lang: ru_RU
metadata.name: dc.subject
metadata.value: мeтoд oпoрных вeктoрoв
metadata.lang: ru_RU
metadata.name: dc.subject
metadata.value: флюoрoгрaфичecкиe cнимки
metadata.lang: ru_RU
metadata.name: dc.subject
metadata.value: соnvоlutiоnаl аutоеncоdеr
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: suppоrt vеctоr mаchinе
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: fluоrоgrаphiс
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Image anomaly detection in preventive fluorographic
metadata.lang: en_GB
metadata.name: dc.title.alternative
metadata.value: Поиск аномалий изображений при профилактическом флюорографическом исследовании
metadata.lang: ru_RU
resources.filename: Tkacheva.pdf
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resources.filename: reviewSV_stt08040_Ronzhin_Aleksandr_Leonidovich_(reviewer)(En).txt
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resources.filename: reviewSV_st007086_Salishhev_Sergej_Igorevich_(supervisor)(En).txt
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resources.filename: reviewSV_st007086_Salishhev_Sergej_Igorevich_(supervisor)(Ru).txt
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