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. The code can be accessed at https://github.com/pranavsinghps1/S4MI.

临床治疗的进步越来越受到监督学习技术的限制，这些技术在很大程度上依赖于大量带注释的数据。注释过程不仅成本高昂，而且需要临床专家投入大量时间。为了解决这个问题，我们引入了 S4MI（医学成像自监督和半监督）管道，这是一种利用自监督和半监督学习进步的新颖方法。这些技术从事不需要标记的辅助任务，因此与完全监督的方法相比，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，以评估它们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中，自监督学习的性能显着超过了监督方法的性能。值得注意的是，半监督方法在分割方面表现出了优异的结果，优于完全监督方法，同时在所有数据集中使用的标签数量减少了 50%。根据我们对科学界做出贡献的承诺，我们公开了 S4MI 代码，从而允许这些方法得到更广泛的应用和进一步发展。该代码可以在 https://github.com/pranavsinghps1/S4MI 访问。