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Titel:

Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Meng, Qingjie; Matthew, Jacqueline; Zimmer, Veronika A; Gomez, Alberto; Lloyd, David F A; Rueckert, Daniel; Kainz, Bernhard
Abstract:
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an interesting and difficult challenge. This problem occurs frequently in medical imaging applications when attempts are made to deploy and improve deep learning models across different image acquisition devices, across acquisition parameters or if some class...     »
Zeitschriftentitel:
IEEE Trans Med Imaging
Jahr:
2021
Band / Volume:
40
Heft / Issue:
2
Seitenangaben Beitrag:
722-734
Volltext / DOI:
doi:10.1109/TMI.2020.3035424
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/33141662
Print-ISSN:
0278-0062
TUM Einrichtung:
Institut für Medizinische Statistik und Epidemiologie
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