Is in-domain data beneficial in transfer learning for landmarks
  detection in x-ray images?
Roberto Di Via Matteo Santacesaria Francesca Odone Vito Paolo Pastore
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.