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.

翻译：临床治疗的进展日益受到监督学习技术局限性的制约，这些技术严重依赖大量注释数据。注释过程不仅成本高昂，还需要临床专家花费大量时间。针对这一问题，我们引入了S4MI（医学成像自监督与半监督）流程，这是一种利用自监督和半监督学习进展的新方法。这些技术涉及无需标注的辅助任务，从而相较于全监督方法简化了机器监督的扩展。我们的研究在三个不同的医学成像数据集上对这些技术进行了基准测试，以评估其在分类和分割任务中的有效性。值得注意的是，我们观察到在所有评估数据集的分类任务中，自监督学习的性能显著超过了监督学习方法。更显著的是，半监督方法在分割任务中展现出更优结果，在所有数据集上使用比全监督方法少50%的标签即取得了更优性能。秉承为科学界贡献的承诺，我们公开了S4MI代码，以便更广泛地应用和进一步开发这些方法。