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

• Published in: Medical image analysis Vol. 72; p. 102136
• Main Authors: He, Yufan, Carass, Aaron, Zuo, Lianrui, Dewey, Blake E., Prince, Jerry L.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: 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

•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...