Medical image segmentation has been significantly advanced by deep learning (DL) techniques, though the data scarcity inherent in medical applications poses a great challenge to DL-based segmentation methods. Self-supervised learning offers a solution by creating auxiliary learning tasks from the available dataset and then leveraging the knowledge acquired from solving auxiliary tasks to help better solve the target segmentation task. Different auxiliary tasks may have different properties and thus can help the target task to different extents. It is desired to leverage their complementary advantages to enhance the overall assistance to the target task. To achieve this, existing methods often adopt a joint training paradigm, which co-solves segmentation and auxiliary tasks by integrating their losses or intermediate gradients. However, direct coupling of losses or intermediate gradients risks undesirable interference because the knowledge acquired from solving each auxiliary task at every training step may not always benefit the target task. To address this issue, we propose a two-stage training approach. In the first stage, the target segmentation task will be independently co-solved with each auxiliary task in both joint training and pre-training modes, with the better model selected via validation performance. In the second stage, the models obtained with respect to each auxiliary task are converted into a single model using an ensemble knowledge distillation method. Our approach allows for making best use of each auxiliary task to create multiple elite segmentation models and then combine them into an even more powerful model. We employed five auxiliary tasks of different proprieties in our approach and applied it to train the U-Net model on an X-ray pneumothorax segmentation dataset. Experimental results demonstrate the superiority of our approach over several existing methods.

翻译：医学图像分割通过深度学习技术取得了显著进展，但医学应用中固有的数据稀缺性给基于深度学习的分割方法带来了巨大挑战。自监督学习通过从可用数据集中创建辅助学习任务，并利用解决辅助任务获得的知识来帮助更好地解决目标分割任务，提供了一种解决方案。不同辅助任务具有不同特性，因此对目标任务的帮助程度各异。理想情况下需利用其互补优势以增强对目标任务的整体支持。现有方法通常采用联合训练范式，通过整合分割任务与辅助任务的损失或中间梯度来共同优化这些任务。然而，直接耦合损失或中间梯度存在不良干扰的风险，因为在每个训练步骤中从解决各辅助任务获得的知识并不总是有利于目标任务。为解决此问题，我们提出了一种两阶段训练方法。第一阶段，目标分割任务将以联合训练和预训练两种模式与每个辅助任务独立协同优化，并通过验证性能选择更优模型。第二阶段，利用集成知识蒸馏方法将对应于各辅助任务的模型转化为单一模型。我们的方法能够充分利用每个辅助任务创建多个精英分割模型，进而将其组合为更强大的模型。我们在方法中采用了五种不同特性的辅助任务，并在X射线气胸分割数据集上训练U-Net模型。实验结果表明，我们的方法优于现有多种方法。