  Image segmentation is a fundamental problem in medical image analysis. In
recent years, deep neural networks achieve impressive performances on many
medical image segmentation tasks by supervised learning on large manually
annotated data. However, expert annotations on big medical datasets are
tedious, expensive or sometimes unavailable. Weakly supervised learning could
reduce the effort for annotation but still required certain amounts of
expertise. Recently, deep learning shows a potential to produce more accurate
predictions than the original erroneous labels. Inspired by this, we introduce
a very weakly supervised learning method, for cystic lesion detection and
segmentation in lung CT images, without any manual annotation. Our method works
in a self-learning manner, where segmentation generated in previous steps
(first by unsupervised segmentation then by neural networks) is used as ground
truth for the next level of network learning. Experiments on a cystic lung
lesion dataset show that the deep learning could perform better than the
initial unsupervised annotation, and progressively improve itself after
self-learning.
