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

Source: [Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification](<https://www.sota2.com/research/paper/68fa7bc51e209131cea8a520>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-28T05:00:13.160Z

Authors: Yucheng Lu, Hubert Dariusz Zaj\\k{a}c, Veronika Cheplygina, Amelia Jim\\'enez-S\\'anchez

Published: 2025-10-01T13:44:46.000Z

## Abstract

Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic principles, which can impact the generalizability of algorithms and, thus, patient outcomes. 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-computer interaction (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. Moreover, ethical and fairness considerations remain largely absent from source dataset sections. Participants often used ambiguous terminology, which suggests a need for clearer definitions and tools to make them explicit and usable. By clarifying these heuristics and introducing a conceptual framework of transfer learning factors, this work provides practical insights for more systematic source selection in transfer learning.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.00902>)

[PDF](<https://arxiv.org/pdf/2510.00902.pdf>)