Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for semi-supervised semantic segmentation. We argue that visually similar regions between labeled and unlabeled images likely contain the same semantics and therefore should share their label. Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set. This way, we avoid pitfalls such as confirmation bias, common in purely prediction-based pseudo-labeling. Since our method does not require any architectural changes or accompanying networks, one can easily insert it into existing frameworks. We achieve the same performance as a standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer labeled images. Aside from an in-depth analysis of different aspects of our proposed method, we further demonstrate the effectiveness of our reference-guided learning paradigm by comparing our approach against existing methods for retinal fluid segmentation with competitive performance as we improve upon recent work by up to 15% mean IoU.
翻译:生成高密度附加说明数据对于医学成像应用来说是一项困难和繁琐的任务。 为了解决这个问题, 我们提出一种新的方法来对半受监督的语义分离进行监管。 我们提出, 标签和无标签图像之间的视觉相似区域可能包含相同的语义学, 因此应该共享它们的标签。 在这样的想法之后, 我们使用少量标签图像作为参考材料, 并将未贴标签的像素与一个参考组中最合适的像素的语义相匹配。 这样, 我们避免了确认偏差等隐患, 这在纯粹基于预测的伪标签中是常见的。 由于我们的方法不需要任何建筑改变或配套网络, 很容易将其插入到现有的框架中。 我们实现同样的性能, 作为一种完全受监督的X射线解剖分离标准模型, 尽管标签图像减少了95%。 除了深入分析我们拟议方法的不同方面外, 我们还通过比较我们现有的再现的液分解方法与竞争性性性性性能的方法, 来进一步展示我们的参考制导学习模式的有效性, 比较我们最近的工作改进了I- 。