Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

Ingrid C. Romero; Shu Kong; Charless C. Fowlkes; Carlos Jaramillo; Michael A. Urban; Francisca Oboh-Ikuenobe

doi:10.1073/pnas.2007324117

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

Ingrid C. Romero
, Shu Kong
, Charless C. Fowlkes
, Carlos Jaramillo
, Michael A. Urban
, Francisca Oboh-Ikuenobe

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	28496-28505
Number of pages	10
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	117
Issue number	45
DOIs	https://doi.org/10.1073/pnas.2007324117
State	Published - Nov 10 2020

ASJC Scopus Subject Areas

General

Keywords

Airyscan microscopy | automated classification | Detarioideae | machine learning | palynology

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10.1073/pnas.2007324117

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keywords = "Airyscan microscopy | automated classification | Detarioideae | machine learning | palynology",

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T1 - Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

AU - Romero, Ingrid C.

AU - Kong, Shu

AU - Fowlkes, Charless C.

AU - Jaramillo, Carlos

AU - Urban, Michael A.

AU - Oboh-Ikuenobe, Francisca

PY - 2020/11/10

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KW - Airyscan microscopy | automated classification | Detarioideae | machine learning | palynology

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U2 - 10.1073/pnas.2007324117

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SN - 0027-8424

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SP - 28496

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JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 45

ER -

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

ASJC Scopus Subject Areas

Keywords

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