Multi-source adversarial transfer learning for ultrasound image
  segmentation with limited similarity
Yifu Zhang Hongru Li Tao Yang Rui Tao Zhengyuan Liu Shimeng Shi Jiansong Zhang Ning Ma Wujin Feng Zhanhu Zhang Xinyu Zhang
Lesion segmentation of ultrasound medical images based on deep learning
techniques is a widely used method for diagnosing diseases. Although there is a
large amount of ultrasound image data in medical centers and other places,
labeled ultrasound datasets are a scarce resource, and it is likely that no
datasets are available for new tissues/organs. Transfer learning provides the
possibility to solve this problem, but there are too many features in natural
images that are not related to the target domain. As a source domain, redundant
features that are not conducive to the task will be extracted. Migration
between ultrasound images can avoid this problem, but there are few types of
public datasets, and it is difficult to find sufficiently similar source
domains. Compared with natural images, ultrasound images have less information,
and there are fewer transferable features between different ultrasound images,
which may cause negative transfer. To this end, a multi-source adversarial
transfer learning network for ultrasound image segmentation is proposed.
Specifically, to address the lack of annotations, the idea of adversarial
transfer learning is used to adaptively extract common features between a
certain pair of source and target domains, which provides the possibility to
utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a
single source domain, multi-source transfer learning is adopted to fuse
knowledge from multiple source domains. In order to ensure the effectiveness of
the fusion and maximize the use of precious data, a multi-source domain
independent strategy is also proposed to improve the estimation of the target
domain data distribution, which further increases the learning ability of the
multi-source adversarial migration learning network in multiple domains.