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Single Image Test-Time Adaptation for Segmentation
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Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation. This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Multiple baselines based on different principles are evaluated under diverse conditions and a novel adversarial training is introduced for adaptation with mask refinement. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines, without these improvements, the increase would be 1.7 and 2.16 % only.
Forward citations
Cited by 2 Pith papers
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Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
Test-time adaptation with a normative atlas prior improves fetal subcortical segmentation in 3D ultrasound, though the main quantitative evaluation is partly circular.
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SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation
A zero-initialized attention adapter placed in SAM's mask decoder matches full fine-tuning on medical segmentation and improves cross-domain generalization with under 1% trainable parameters.
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