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Advancements in clinical treatment are increasingly constrained by thelimitations of supervised learning techniques, which depend heavily on largevolumes of annotated data. The annotation process is not only costly but alsodemands substantial time from clinical specialists. Addressing this issue, weintroduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging)pipeline, a novel approach that leverages the advancements in self-supervisedand semi-supervised learning. These techniques engage in auxiliary tasks thatdo not require labeling, thus simplifying the scaling of machine supervisioncompared to fully-supervised methods. Our study benchmarks these techniques onthree distinct medical imaging datasets to evaluate their effectiveness inclassification and segmentation tasks. Remarkably, we observed thatself-supervised learning with only 10% of the annotation surpassed theperformance of full annotation in the classification of most datasets.Similarly, the semi-supervised approach demonstrated superior outcomes insegmentation, outperforming fully-supervised methods with 50% fewer labelsacross all datasets. In line with our commitment to contributing to thescientific community, we have made the S4MI code openly accessible, allowingfor broader application and further development of these methods.

Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification