Breaking the Single-Stage Barrier: Synergistic Data-Model Adaptation at Test-Time for Medical Image Segmentation

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dc.contributor.authorZhou, Wenjuanen_US
dc.contributor.authorChen, Weien_US
dc.contributor.authorHe, Yulinen_US
dc.contributor.authorWu, Dien_US
dc.contributor.authorLi, Chenen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:03:25Z
dc.date.available2025-10-07T06:03:25Z
dc.date.issued2025
dc.description.abstractDomain shift, predominantly caused by variations in medical imaging across different institutions, often leads to a decline in the accuracy of medical image segmentation models. While Test-Time Adaptation (TTA) holds promise to address this issue, existing methods exhibit significant limitations: model adaptation is prone to error accumulation and catastrophic forgetting in continuous domain learning. Meanwhile, data adaptation struggles to achieve deep latent alignment due to the inaccessibility of source domain data. To address these challenges, we propose Synergistic Data-Model Adaptation (SDMA), which innovatively leverages Batch Normalization (BN) layers as a bidirectional bridge to enable a two-stage joint adaptation process. In the data adaptation stage, domain-aware prompts dynamically adjust the BN statistics of incoming test data, achieving low-level distribution alignment in the Fourier space. In the model adaptation stage, we dynamically optimize the BN affine parameters based on strong-weak data augmentation and entropy minimization, enabling adaptation to high-level semantic features. Experiments conducted on five retinal fundus image datasets from various medical institutions demonstrate that our method achieves an average Dice improvement of 1.23% over previous state-of-the-art (SOTA) methods, establishing a new SOTA performance.en_US
dc.description.sectionheadersDetecting & Estimating from images
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251273
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251273
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251273
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image segmentation
dc.subjectComputing methodologies → Image segmentation
dc.titleBreaking the Single-Stage Barrier: Synergistic Data-Model Adaptation at Test-Time for Medical Image Segmentationen_US
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