  Self-supervised learning (SSL) has emerged as a promising technique for
medical image analysis due to its ability to learn without annotations.
However, despite the promising potential, conventional SSL methods encounter
limitations, including challenges in achieving semantic alignment and capturing
subtle details. This leads to suboptimal representations, which fail to
accurately capture the underlying anatomical structures and pathological
details. In response to these constraints, we introduce a novel SSL framework
OPTiML, employing optimal transport (OT), to capture the dense semantic
invariance and fine-grained details, thereby enhancing the overall
effectiveness of SSL in medical image representation learning. The core idea is
to integrate OT with a cross-viewpoint semantics infusion module (CV-SIM),
which effectively captures complex, fine-grained details inherent in medical
images across different viewpoints. In addition to the CV-SIM module, OPTiML
imposes the variance and covariance regularizations within OT framework to
force the model focus on clinically relevant information while discarding less
informative features. Through these, the proposed framework demonstrates its
capacity to learn semantically rich representations that can be applied to
various medical imaging tasks. To validate its effectiveness, we conduct
experimental studies on three publicly available datasets from chest X-ray
modality. Our empirical results reveal OPTiML's superiority over
state-of-the-art methods across all evaluated tasks.
