论文标题
基于嘈杂的伪标签和对抗性学习的医学图像分割的注释效率学习
Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning
论文作者
论文摘要
尽管深度学习已经实现了医学图像细分的最新性能，但其成功依赖于一大批手动注释的图像，用于培训昂贵的培训。在本文中，我们为分割任务提出了一个避免训练图像注释的注释学习框架，在该框架中，我们使用改进的周期一致的生成对抗网络（GAN）从一组未配对的医学图像和从形状模型或公共数据集中获得的辅助膜从一组未配对的医学图像中学习。 We first use the GAN to generate pseudo labels for our training images under the implicit high-level shape constraint represented by a Variational Auto-encoder (VAE)-based discriminator with the help of the auxiliary masks, and build a Discriminator-guided Generator Channel Calibration (DGCC) module which employs our discriminator's feedback to calibrate the generator for better pseudo labels.要从嘈杂的伪标签中学习，我们进一步引入了一种使用噪声加权骰子丢失的噪声迭代学习方法。我们使用两种情况验证了框架：具有简单形状模型的对象，例如底眼图像中的光盘和超声图像中的胎头，以及X射线图像中的肺和CT图像中的肺部等复杂结构。实验结果表明，1）我们基于VAE的鉴别器和DGCC模块有助于获得高质量的伪标签。 2）我们提出的噪声学习方法可以有效地克服嘈杂的伪标签的效果。 3）我们的方法的分割性能不使用训练图像的注释，甚至与从人类注释中学习的图像相当。
Despite that deep learning has achieved state-of-the-art performance for medical image segmentation, its success relies on a large set of manually annotated images for training that are expensive to acquire. In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets. We first use the GAN to generate pseudo labels for our training images under the implicit high-level shape constraint represented by a Variational Auto-encoder (VAE)-based discriminator with the help of the auxiliary masks, and build a Discriminator-guided Generator Channel Calibration (DGCC) module which employs our discriminator's feedback to calibrate the generator for better pseudo labels. To learn from the pseudo labels that are noisy, we further introduce a noise-robust iterative learning method using noise-weighted Dice loss. We validated our framework with two situations: objects with a simple shape model like optic disc in fundus images and fetal head in ultrasound images, and complex structures like lung in X-Ray images and liver in CT images. Experimental results demonstrated that 1) Our VAE-based discriminator and DGCC module help to obtain high-quality pseudo labels. 2) Our proposed noise-robust learning method can effectively overcome the effect of noisy pseudo labels. 3) The segmentation performance of our method without using annotations of training images is close or even comparable to that of learning from human annotations.
