Deep convolutional neural networks have driven substantial advancements in
the automatic understanding of images. Requiring a large collection of images
and their associated annotations is one of the main bottlenecks limiting the
adoption of deep networks. In the task of medical image segmentation, requiring
pixel-level semantic annotations performed by human experts exacerbate this
difficulty. This paper proposes a new framework to train a fully convolutional
segmentation network from a large set of cheap unreliable annotations and a
small set of expert-level clean annotations. We propose a spatially adaptive
reweighting approach to treat clean and noisy pixel-level annotations
commensurately in the loss function. We deploy a meta-learning approach to
assign higher importance to pixels whose loss gradient direction is closer to
those of clean data. Our experiments on training the network using segmentation
ground truth corrupted with different levels of annotation noise show how
spatial reweighting improves the robustness of deep networks to noisy
annotations.

该研究提出了一个新的框架，可以使用大量的便宜的不可靠注释和较少的专家级别的干净注释训练完全卷积分割网络，使用空间可适应重新加权方法在损失函数中平衡了干净和嘈杂的像素级注释，使用元学习方法将更多的重要性分配给损失梯度方向更接近干净数据的像素，实验表明，使用空间重新加权可以改善深度网络对噪声注释的鲁棒性。