Abstract
Transfer learning is crucial for medical imaging, yet the selection of source datasets - which can impact the generalizability of algorithms, and thus patient outcomes - often relies on researchers' intuition rather than systematic principles. This study investigates these decisions through a task-based survey with machine learning practitioners. Unlike prior work that benchmarks models and experimental setups, we take a human-centered HCI perspective on how practitioners select source datasets. Our findings indicate that choices are task-dependent and influenced by community practices, dataset properties, and computational (data embedding), or perceived visual or semantic similarity. However, similarity ratings and expected performance are not always aligned, challenging a traditional "more similar is better" view. Participants often used ambiguous terminology, which suggests a need for clearer definitions and HCI tools to make them explicit and usable. By clarifying these heuristics, this work provides practical insights for more systematic source selection in transfer learning.
Abstract (translated)
迁移学习在医学影像领域至关重要，但选择源数据集——这会影响算法的泛化能力，进而影响患者结果——往往依赖于研究人员的直觉而非系统的准则。本研究通过面向机器学习从业者的任务调研来探讨这些决策。与以往侧重模型基准和实验设置的研究不同，我们从以人为本的人机交互（HCI）视角出发，考察从业者选择源数据集的方式。我们的发现表明，选择取决于具体任务，并受到社区实践、数据集属性以及计算嵌入或感知的视觉或语义相似性的影响。然而，相似性的评分与预期性能并不总是保持一致，这挑战了传统的“越相似越好”的观点。 参与者经常使用含糊不清的技术术语，这一点提示我们需要更清晰的定义和人机交互工具，以使这些概念明确且易于理解。通过阐明这些启发式方法，本研究为迁移学习中更具系统性的源数据集选择提供了实际见解。
URL
https://arxiv.org/abs/2510.00902
