Self-supervised learning (SSL) has emerged as a powerful strategy for representation learning under limited annotation regimes, yet its effectiveness remains highly sensitive to many factors, especially the nature of the target task. In segmentation, existing pipelines are typically tuned to large, homogeneous regions, but their performance drops when objects are small, sparse, or locally irregular. In this work, we propose a scale-aware SSL adaptation that integrates small-window cropping into the augmentation pipeline, zooming in on fine-scale structures during pretraining. We evaluate this approach across two domains with markedly different data modalities: seismic imaging, where the goal is to segment sparse faults, and neuroimaging, where the task is to delineate small cellular structures. In both settings, our method yields consistent improvements over standard and state-of-the-art baselines under label constraints, improving accuracy by up to 13% for fault segmentation and 5% for cell delineation. In contrast, large-scale features such as seismic facies or tissue regions see little benefit, underscoring that the value of SSL depends critically on the scale of the target objects. Our findings highlight the need to align SSL design with object size and sparsity, offering a general principle for buil ding more effective representation learning pipelines across scientific imaging domains.

翻译：自监督学习（SSL）已成为有限标注条件下表示学习的强大策略，但其有效性仍对诸多因素高度敏感，尤其是目标任务的特性。在分割任务中，现有流程通常针对大而均匀的区域进行优化，但当目标物体尺寸小、分布稀疏或局部不规则时，其性能会显著下降。本研究提出一种尺度感知的自监督学习改进方法，通过在小窗口裁剪操作融入数据增强流程，在预训练阶段聚焦于细粒度结构。我们在两种数据模态迥异的领域评估了该方法的有效性：地震成像（目标为分割稀疏断层）和神经影像学（任务为勾勒微小细胞结构）。在这两种场景下，在标注受限条件下，我们的方法相较于标准方法和前沿基线模型均取得了稳定提升，断层分割精度最高提升13%，细胞轮廓勾勒精度提升5%。相比之下，大规模特征（如地震相或组织区域）的改善微乎其微，这凸显出自监督学习的价值本质上取决于目标物体的尺度特性。我们的研究结果强调，需要使自监督学习的设计与目标物体的尺寸及稀疏性相匹配，这为构建跨科学影像领域更有效的表示学习流程提供了普适性原则。