  Local discriminative representation is needed in many medical image analysis
tasks such as identifying sub-types of lesion or segmenting detailed components
of anatomical structures. However, the commonly applied supervised
representation learning methods require a large amount of annotated data, and
unsupervised discriminative representation learning distinguishes different
images by learning a global feature, both of which are not suitable for
localized medical image analysis tasks. In order to avoid the limitations of
these two methods, we introduce local discrimination into unsupervised
representation learning in this work. The model contains two branches: one is
an embedding branch which learns an embedding function to disperse dissimilar
pixels over a low-dimensional hypersphere; and the other is a clustering branch
which learns a clustering function to classify similar pixels into the same
cluster. These two branches are trained simultaneously in a mutually beneficial
pattern, and the learnt local discriminative representations are able to well
measure the similarity of local image regions. These representations can be
transferred to enhance various downstream tasks. Meanwhile, they can also be
applied to cluster anatomical structures from unlabeled medical images under
the guidance of topological priors from simulation or other structures with
similar topological characteristics. The effectiveness and usefulness of the
proposed method are demonstrated by enhancing various downstream tasks and
clustering anatomical structures in retinal images and chest X-ray images.
