  Contrastive learning, which aims to capture general representation from
unlabeled images to initialize the medical analysis models, has been proven
effective in alleviating the high demand for expensive annotations. Current
methods mainly focus on instance-wise comparisons to learn the global
discriminative features, however, pretermitting the local details to
distinguish tiny anatomical structures, lesions, and tissues. To address this
challenge, in this paper, we propose a general unsupervised representation
learning framework, named local discrimination (LD), to learn local
discriminative features for medical images by closely embedding semantically
similar pixels and identifying regions of similar structures across different
images. Specifically, this model is equipped with an embedding module for
pixel-wise embedding and a clustering module for generating segmentation. And
these two modules are unified through optimizing our novel region
discrimination loss function in a mutually beneficial mechanism, which enables
our model to reflect structure information as well as measure pixel-wise and
region-wise similarity. Furthermore, based on LD, we propose a center-sensitive
one-shot landmark localization algorithm and a shape-guided cross-modality
segmentation model to foster the generalizability of our model. When
transferred to downstream tasks, the learned representation by our method shows
a better generalization, outperforming representation from 18 state-of-the-art
(SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the
challenging lesion segmentation tasks, the proposed method achieves
significantly better performances. The source codes are publicly available at
https://github.com/HuaiChen-1994/LDLearning.
