Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
This addresses the challenge of limited annotated data for medical image analysis, offering guidance for developing robust landmark detection systems in x-ray images, though it is incremental as it confirms existing trends.
The study investigated whether using small-scale in-domain x-ray datasets improves landmark detection over models pre-trained on large natural image datasets like ImageNet, finding that in-domain data provides marginal or no benefit.
In recent years, deep learning has emerged as a promising technique for medical image analysis. However, this application domain is likely to suffer from a limited availability of large public datasets and annotations. A common solution to these challenges in deep learning is the usage of a transfer learning framework, typically with a fine-tuning protocol, where a large-scale source dataset is used to pre-train a model, further fine-tuned on the target dataset. In this paper, we present a systematic study analyzing whether the usage of small-scale in-domain x-ray image datasets may provide any improvement for landmark detection over models pre-trained on large natural image datasets only. We focus on the multi-landmark localization task for three datasets, including chest, head, and hand x-ray images. Our results show that using in-domain source datasets brings marginal or no benefit with respect to an ImageNet out-of-domain pre-training. Our findings can provide an indication for the development of robust landmark detection systems in medical images when no large annotated dataset is available.