On dataset transferability in medical image classification
Journal:
arXiv
Published Date:
Dec 28, 2024
Abstract
Current transferability estimation methods designed for natural image
datasets are often suboptimal in medical image classification. These methods
primarily focus on estimating the suitability of pre-trained source model
features for a target dataset, which can lead to unrealistic predictions, such
as suggesting that the target dataset is the best source for itself. To address
this, we propose a novel transferability metric that combines feature quality
with gradients to evaluate both the suitability and adaptability of source
model features for target tasks. We evaluate our approach in two new scenarios:
source dataset transferability for medical image classification and
cross-domain transferability. Our results show that our method outperforms
existing transferability metrics in both settings. We also provide insight into
the factors influencing transfer performance in medical image classification,
as well as the dynamics of cross-domain transfer from natural to medical
images. Additionally, we provide ground-truth transfer performance benchmarking
results to encourage further research into transferability estimation for
medical image classification. Our code and experiments are available at
https://github.com/DovileDo/transferability-in-medical-imaging.
