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Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation

Research Medical/Healthcare AI

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TL;DR - This paper uses prediction fragmentation—the disagreement geometry between original and adapted segmentation masks—to decide whether each medical-imaging case should undergo test-time adaptation. The approach substantially reduces harmful adaptations without requiring labels or additional backward passes at decision time.

  • Fixed-step adaptation worsened 58.7% of cases on cross-vendor cardiac MRI despite producing no statistically significant mean Dice change.
  • Prediction fragmentation correlates with harmful accepted area across three benchmarks (Spearman ρ 0.50–0.60) at one-quarter the latency of a gradient-norm signal.
  • On the selected cardiac benchmark, the router cut harmful accepted area from 0.129 to 0.013 at matched Dice and reduced harmed cases from 58.7% to 20.0%.
  • The routing template transferred across segmentation architectures and domains, though on prostate data it reduced harmful edits at the cost of accuracy.

Sources (1)

Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation

arXiv cs.CV Lili Wang, Jing Li, Xiaowen Sun, Xiangyu Hu, Zhuangzhuang Gu, Jian Liu, Srihari Nelakuditi, Yan Tong 2026-09-17 arXiv:2609.20700
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-21 14:17:13.106905 UTC

TL;DR - This paper uses prediction fragmentation—the disagreement geometry between original and adapted segmentation masks—to decide whether each medical-imaging case should undergo test-time adaptation. The approach substantially reduces harmful adaptations without requiring labels or additional backward passes at decision time.

  • Fixed-step adaptation worsened 58.7% of cases on cross-vendor cardiac MRI despite producing no statistically significant mean Dice change.
  • Prediction fragmentation correlates with harmful accepted area across three benchmarks (Spearman ρ 0.50–0.60) at one-quarter the latency of a gradient-norm signal.
  • On the selected cardiac benchmark, the router cut harmful accepted area from 0.129 to 0.013 at matched Dice and reduced harmed cases from 58.7% to 20.0%.
  • The routing template transferred across segmentation architectures and domains, though on prostate data it reduced harmful edits at the cost of accuracy.
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