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BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

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Title: BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.
Authors: Yang, Helen Y1,2 (AUTHOR) helen_yang@fas.harvard.edu, Leahy, Brian D1,3 (AUTHOR), Jang, Won-Dong4 (AUTHOR), Wei, Donglai4 (AUTHOR), Kalma, Yael5 (AUTHOR), Rahav, Roni5 (AUTHOR), Carmon, Ariella5 (AUTHOR), Kopel, Rotem5 (AUTHOR), Azem, Foad5 (AUTHOR), Venturas, Marta1 (AUTHOR), Kelleher, Colm P1 (AUTHOR), Cam, Liz1 (AUTHOR), Pfister, Hanspeter4 (AUTHOR), Needleman, Daniel J1,3 (AUTHOR), Ben-Yosef, Dalit5,6 (AUTHOR)
Source: Human Reproduction. Apr2024, Vol. 39 Issue 4, p698-708. 11p.
Database: Academic Search Ultimate
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  Data: <searchLink fieldCode="JN" term="%22Human+Reproduction%22">Human Reproduction</searchLink>. Apr2024, Vol. 39 Issue 4, p698-708. 11p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=176404353
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        Value: 10.1093/humrep/deae024
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 698
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      – TitleFull: BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.
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              Text: Apr2024
              Type: published
              Y: 2024
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              Value: 39
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            – TitleFull: Human Reproduction
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