  Advancements in clinical treatment are increasingly constrained by the
limitations of supervised learning techniques, which depend heavily on large
volumes of annotated data. The annotation process is not only costly but also
demands substantial time from clinical specialists. Addressing this issue, we
introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging)
pipeline, a novel approach that leverages advancements in self-supervised and
semi-supervised learning. These techniques engage in auxiliary tasks that do
not require labeling, thus simplifying the scaling of machine supervision
compared to fully-supervised methods. Our study benchmarks these techniques on
three distinct medical imaging datasets to evaluate their effectiveness in
classification and segmentation tasks. Notably, we observed that self
supervised learning significantly surpassed the performance of supervised
methods in the classification of all evaluated datasets. Remarkably, the
semi-supervised approach demonstrated superior outcomes in segmentation,
outperforming fully-supervised methods while using 50% fewer labels across all
datasets. In line with our commitment to contributing to the scientific
community, we have made the S4MI code openly accessible, allowing for broader
application and further development of these methods.
