SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis

Key points

• However, existing AL strategies fail to account for the spatial distribution of selected samples, resulting in inconsistent performance across focal (e.g., aortic calcification) and diffuse (e.g., nodules) pathologies.1
• In this paper, we propose SPARCL (Spatially Adaptive Regional Coverage for Active Learning) to dynamically balance predictive uncertainty with cumulative spatial coverage.1
• By employing a concave coverage function, SPARCL imposes diminishing returns on previously annotated regions, naturally directing the annotation budget toward under-labeled areas.1
• To address how aggregate metrics like Dice obscure regional failures, we further introduce two metrics---Spatial Inconsistency (SI) and Performance Coverage (PC)---to quantify anatomical fairness across AL rounds.1

Sources (1)

• [1]SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:34 AM

  Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential.

  However, existing AL strategies fail to account for the spatial distribution of selected samples, resulting in inconsistent performance across focal (e.g., aortic calcification) and diffuse (e.g., nodules) pathologies.

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