  Weakly-supervised learning has attracted growing research attention on
medical lesions segmentation due to significant saving in pixel-level
annotation cost. However, 1) most existing methods require effective prior and
constraints to explore the intrinsic lesions characterization, which only
generates incorrect and rough prediction; 2) they neglect the underlying
semantic dependencies among weakly-labeled target enteroscopy diseases and
fully-annotated source gastroscope lesions, while forcefully utilizing
untransferable dependencies leads to the negative performance. To tackle above
issues, we propose a new weakly-supervised lesions transfer framework, which
can not only explore transferable domain-invariant knowledge across different
datasets, but also prevent the negative transfer of untransferable
representations. Specifically, a Wasserstein quantified transferability
framework is developed to highlight widerange transferable contextual
dependencies, while neglecting the irrelevant semantic characterizations.
Moreover, a novel selfsupervised pseudo label generator is designed to equally
provide confident pseudo pixel labels for both hard-to-transfer and
easyto-transfer target samples. It inhibits the enormous deviation of false
pseudo pixel labels under the self-supervision manner. Afterwards,
dynamically-searched feature centroids are aligned to narrow category-wise
distribution shift. Comprehensive theoretical analysis and experiments show the
superiority of our model on the endoscopic dataset and several public datasets.
