Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification

• URL: http://arxiv.org/abs/2510.00902v1
• Date: Wed, 01 Oct 2025 13:44:46 GMT
• Title: Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
• Authors: Yucheng Lu, Hubert Dariusz Zając, Veronika Cheplygina, Amelia Jiménez-Sánchez,
• Abstract summary: This study investigates how machine learning practitioners select source datasets.<n>Our findings indicate that choices are task-dependent and influenced by community practices, dataset properties, and computational (data embedding)<n>Participants often used ambiguous terminology, which suggests a need for clearer definitions and HCI tools.
• Score: 9.85496186685158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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