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  1. 1. On Medical Image Segmentation With Noisy Labels

    Författare :Marcus Nordström; Henrik Hult; Atsuto Maki; Fredrik Kahl; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Medical image segmentation; image segmentation; machine learning; supervised learning; label noise; Sörensen-Dice coefficient; soft-Dice; Medicinsk bildsegmentering; bildsegmentering; maskininlärning; övervakat lärande; annoteringsbrus; Sörensen-Dice koefficienten; soft-Dice; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics; Mathematical Statistics; Matematisk statistik;

    Sammanfattning : It is well known that data sets used for training and testing automatic medical image segmentation methods often contain a lot of label noise. Such noise affects the performance of the methods and has been subject to a lot of research. LÄS MER