Single Image Test-Time Adaptation via Multi-View Co-Training

• URL: http://arxiv.org/abs/2506.23705v1
• Date: Mon, 30 Jun 2025 10:29:33 GMT
• Title: Single Image Test-Time Adaptation via Multi-View Co-Training
• Authors: Smriti Joshi, Richard Osuala, Lidia Garrucho, Kaisar Kushibar, Dimitri Kessler, Oliver Diaz, Karim Lekadir,
• Abstract summary: We propose a Patch-Based Multi-View Co-Training method for Single Image Test-Time adaptation.<n>Our method enforces feature and prediction consistency through uncertainty-guided self-training.<n>Our method achieves performance close to the upper bound supervised benchmark.
• Score: 1.73329304643509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time adaptation enables a trained model to adjust to a new domain during inference, making it particularly valuable in clinical settings where such on-the-fly adaptation is required. However, existing techniques depend on large target domain datasets, which are often impractical and unavailable in medical scenarios that demand per-patient, real-time inference. Moreover, current methods commonly focus on two-dimensional images, failing to leverage the volumetric richness of medical imaging data. Bridging this gap, we propose a Patch-Based Multi-View Co-Training method for Single Image Test-Time adaptation. Our method enforces feature and prediction consistency through uncertainty-guided self-training, enabling effective volumetric segmentation in the target domain with only a single test-time image. Validated on three publicly available breast magnetic resonance imaging datasets for tumor segmentation, our method achieves performance close to the upper bound supervised benchmark while also outperforming all existing state-of-the-art methods, on average by a Dice Similarity Coefficient of 3.75%. We publicly share our accessible codebase, readily integrable with the popular nnUNet framework, at https://github.com/smriti-joshi/muvi.git.

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