Knowledge Transfer across Diseases

In the field of medical data mining, collecting sufficient expert labeled datasets especially with pixel-level annotations is extremely difficult. For diseases with scarce data, supervised learning has little effect even with synthetic images for data augmentation or cross-modality knowledge transfer. In response to the problem, we propose a novel solution - knowledge transfer across diseases. Existing transfer methods either focus on mapping one domain to another or focus on generating a subspace consisting of domain invariant features. However, these methods are negative in the transfer of knowledge across diseases because they lose the private characteristics of the target disease. Motivated by this limitation, we present a reweighting network (RW-net). It can be widely applied to a domain adaptation model based on adversarial, through reweighting dynamic balance subspace generation and segmenter optimization. We compare our method to two popular transfer learning methods and baseline on the open glioma dataset (BraTS) and ischemic stroke dataset (ATLAS). Our approach achieves excellent performance, even though the two diseases have completely different imaging traits.