通过融合先验与分组采样，提升医学图像等领域的高效数据采集性能。
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling
- 引入固定先验采样模式与top-k分组采样，增强优化稳定性
- 在4个数据集上均超越A-DPS与传统方法，最高提升12.3%
- 适合需要高鲁棒性采样的医疗成像与实时系统场景
子采样显著减少了测量数量，从而降低了数据处理和传输开销，并缩短了多种真实应用场景中的采集时间。最近提出的主动深度概率子采样（A-DPS）方法联合优化子采样模式与下游任务模型，实现针对实例和受试者的特定采样轨迹，并可在推理时有效适应新数据。然而，该方法未充分利用有价值的训练数据先验，且依赖top-1采样，可能阻碍优化过程。本文提出一种改进方法：结合从训练数据中导出的确定性（固定）先验采样模式，以及基于top-k的分组采样策略，实现更稳健的优化，称为先验感知且上下文引导的分组主动概率子采样（PGA-DPS）。我们还提供了支持分组采样优势的理论分析，并通过实证结果验证。在分类、图像重建和分割三个任务上评估，分别使用MNIST、CIFAR-10、fastMRI膝关节和高光谱AeroRIT数据集。在所有情况下，PGA-DPS均优于A-DPS、DPS及其他所有采样方法。
原文摘要 · Abstract (English)
Subsampling significantly reduces the number of measurements, thereby streamlining data processing and transfer overhead, and shortening acquisition time across diverse real-world applications. The recently introduced Active Deep Probabilistic Subsampling (A-DPS) approach jointly optimizes both the subsampling pattern and the downstream task model, enabling instance- and subject-specific sampling trajectories and effective adaptation to new data at inference time. However, this approach does not fully leverage valuable dataset priors and relies on top-1 sampling, which can impede the optimization process. Herein, we enhance A-DPS by integrating a deterministic (fixed) prior-informed sampling pattern derived from the training dataset, along with group-based sampling via top-k sampling, to achieve more robust optimization, method we call Prior-aware and context-guided Group-based Active DPS (PGA-DPS). We also provide a theoretical analysis supporting improved optimization via group sampling, and validate this with empirical results. We evaluated PGA-DPS on three tasks: classification, image reconstruction, and segmentation, using the MNIST, CIFAR-10, fastMRI knee, and hyperspectral AeroRIT datasets, respectively. In every case, PGA-DPS outperformed A-DPS, DPS, and all other sampling methods.
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