Autoencoder based self-supervised test-time adaptation for medical image analysis

•A novel formulation for domain adaptation without retraining the model nor using source training data.•Quick test-time adaptation to a single test subject at inference stage.•Applicable to both medical image synthesis and segmentation. [Display omitted] Deep neural networks have been successfully a...

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Veröffentlicht in:	Medical image analysis Jg. 72; S. 102136
Hauptverfasser:	He, Yufan, Carass, Aaron, Zuo, Lianrui, Dewey, Blake E., Prince, Jerry L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.08.2021 Elsevier BV
Schlagworte:	Adapters Artificial neural networks Datasets Deep learning Domains Image analysis Image processing Image Processing, Computer-Assisted Image reconstruction Image segmentation Machine learning Magnetic Resonance Imaging Medical image analysis Medical imaging Neural networks Neural Networks, Computer Optimization Retina Self supervised learning Synthesis Test time adaptation Testing time Tomography, Optical Coherence Training Unsupervised domain adaptation Self supervised learning 68T45 Medical image analysis 41A10 Unsupervised domain adaptation 65D05 65D17 Test time adaptation 41A05
ISSN:	1361-8415, 1361-8423, 1361-8423
Online-Zugang:	Volltext
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