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.

医学图像分割使用深度学习技术取得了重大进展，然而医学应用中固有的数据稀缺性对深度学习的分割方法构成了巨大挑战。自监督学习通过从可用数据集中创建辅助学习任务，并利用解决这些任务获得的知识来更好地解决目标分割任务，提供了一种解决方案。本研究提出了一种两阶段训练方法，可以有效利用多个辅助任务创建多个出色的分割模型，然后将它们合并为一个更强大的模型。该方法在 X 光气胸分割数据集上训练了 U-Net 模型，并对几种现有方法进行了实验比较，结果表明了方法的优越性。

医学图像分割的两阶段多任务自监督学习

Two-Stage Multi-task Self-Supervised Learning for Medical Image  Segmentation

Recently, deep learning-based facial landmark detection for in-the-wild faces
has achieved significant improvement. However, there are still challenges in
face landmark detection in other domains (e.g. cartoon, caricature, etc). This
is due to the scarcity of extensively annotated training data. To tackle this
concern, we design a two-stage training approach that effectively leverages
limited datasets and the pre-trained diffusion model to obtain aligned pairs of
landmarks and face in multiple domains. In the first stage, we train a
landmark-conditioned face generation model on a large dataset of real faces. In
the second stage, we fine-tune the above model on a small dataset of
image-landmark pairs with text prompts for controlling the domain. Our new
designs enable our method to generate high-quality synthetic paired datasets
from multiple domains while preserving the alignment between landmarks and
facial features. Finally, we fine-tuned a pre-trained face landmark detection
model on the synthetic dataset to achieve multi-domain face landmark detection.
Our qualitative and quantitative results demonstrate that our method
outperforms existing methods on multi-domain face landmark detection.

利用两阶段训练方法和合成配对数据集，在多个领域中实现了高质量的多领域人脸特征点检测，并优于现有方法。