  Representation learning constitutes a pivotal cornerstone in contemporary
deep learning paradigms, offering a conduit to elucidate distinctive features
within the latent space and interpret the deep models. Nevertheless, the
inherent complexity of anatomical patterns and the random nature of lesion
distribution in medical image segmentation pose significant challenges to the
disentanglement of representations and the understanding of salient features.
Methods guided by the maximization of mutual information, particularly within
the framework of contrastive learning, have demonstrated remarkable success and
superiority in decoupling densely intertwined representations. However, the
effectiveness of contrastive learning highly depends on the quality of the
positive and negative sample pairs, i.e. the unselected average mutual
information among multi-views would obstruct the learning strategy so the
selection of the views is vital. In this work, we introduce a novel approach
predicated on representation distance-based mutual information (MI)
maximization for measuring the significance of different views, aiming at
conducting more efficient contrastive learning and representation
disentanglement. Additionally, we introduce an MI re-ranking strategy for
representation selection, benefiting both the continuous MI estimating and
representation significance distance measuring. Specifically, we harness
multi-view representations extracted from the frequency domain, re-evaluating
their significance based on mutual information across varying frequencies,
thereby facilitating a multifaceted contrastive learning approach to bolster
semantic comprehension. The statistical results under the five metrics
demonstrate that our proposed framework proficiently constrains the MI
maximization-driven representation selection and steers the multi-view
contrastive learning process.
