论文标题
定向梯度的直方图符合深度学习：一种新型的多任务深网络，用于医学图像语义分割
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
论文作者
论文摘要
我们介绍了用于医学图像分割的新颖的深度多任务学习方法。现有的多任务方法需要针对主要和辅助任务的地面真相注释。与之相反，我们建议以无监督的方式生成辅助任务的伪标签。为了生成伪标签，我们利用定向梯度（HOGS）的直方图，这是使用最广泛和最强大的手工制作的特征之一。与辅助任务的主要任务和伪标记的地面真理语义分段掩盖一起，我们学习了深网的参数，以最大程度地减少主要任务和辅助任务的损失。我们在两个功能强大且广泛使用的语义分割网络上采用了我们的方法：UNET和U2NET在多任务设置中进行训练。为了验证我们的假设，我们对两个不同的医学图像分割数据集进行了实验。从广泛的定量和定性结果来看，我们观察到与反零件相比，我们的方法一致地提高了性能。此外，我们的方法是与MICCAI 2021结合组织的语义分割的FETREG ENDOVIS ENDOVIS子挑战的赢家。
We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an auxiliary task in an unsupervised manner. To generate the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of the most widely used and powerful hand-crafted features for detection. Together with the ground truth semantic segmentation masks for the primary task and pseudo-labels for the auxiliary task, we learn the parameters of the deep network to minimise the loss of both the primary task and the auxiliary task jointly. We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup. To validate our hypothesis, we performed experiments on two different medical image segmentation data sets. From the extensive quantitative and qualitative results, we observe that our method consistently improves the performance compared to the counter-part method. Moreover, our method is the winner of FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction with MICCAI 2021.
