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 the 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.

翻译：临床治疗技术的进步日益受到监督学习技术局限性的制约，其严重依赖大量标注数据。标注过程不仅成本高昂，更需要临床专家投入大量时间。针对这一难题，我们提出S4MI（医学影像自监督与半监督）流程，这是一种利用自监督和半监督学习进展的创新方法。这些技术通过执行无需标签的辅助任务，相比全监督方法能够简化机器监督的扩展过程。本研究在三个不同的医学影像数据集上对上述技术进行基准测试，评估其在分类与分割任务中的有效性。值得注意的是，我们发现自监督学习在所有评估数据集的分类任务中均显著超越监督方法。更为突出的是，半监督方法在分割任务中展现出卓越性能，其在使用比全监督方法少50%标签的情况下，在所有数据集上均取得了更优结果。秉持对科学共同体的贡献承诺，我们已公开S4MI代码，以促进这些方法的广泛应用与进一步开发。