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Found 3 papers, 3 papers with code
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e. g., when the sets are produced by different hardware.
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data.
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Our results demonstrate that 1) domain-shift may deteriorate the quality even for a simple brain extraction segmentation task (surface Dice Score drops from 0. 85-0. 89 even to 0. 09); 2) fine-tuning of the first layers significantly outperforms fine-tuning of the last layers in almost all supervised domain adaptation setups.
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