Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning

scientific article published on 08 May 2019

Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1371/JOURNAL.PONE.0216493
P932PMC publication ID6505961
P698PubMed publication ID31067239

P50authorTalal Al-SammanQ58323501
Sandra Korte-KerzelQ58659836
P2093author name stringCarl Kusche
Martina Freund
Tom Reclik
Ulrich Kerzel
P2860cites workHigh Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel.Q64924876
Dermatologist-level classification of skin cancer with deep neural networksQ28528865
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosisQ36925087
Advanced Steel Microstructural Classification by Deep Learning Methods.Q49314101
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P4510describes a project that usesscikit-learnQ1026367
scikit-imageQ22442795
deep learningQ197536
P433issue5
P407language of work or nameEnglishQ1860
P921main subjectdeep learningQ197536
P304page(s)e0216493
P577publication date2019-05-08
P1433published inPLOS OneQ564954
P1476titleLarge-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning
P478volume14

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Q91151721Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Networkcites workP2860

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