论文标题
半监督的几射击显微镜图像细胞分割的边缘基于边缘的自我审视
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
论文作者
论文摘要
深层神经网络目前为显微镜图像细胞分割提供了令人鼓舞的结果，但它们需要大规模标记的数据库，这是一个昂贵且耗时的过程。在这项工作中，我们通过将自我监督与半监督的学习相结合来放松标签要求。我们提出了基于边缘的地图的预测，以自我监督未标记的图像的训练，该图像与少数标记图像的监督培训相结合，以学习分割任务。在我们的实验中，我们在几个光学图像细胞分割基准上进行了评估，并表明只有少数带注释的图像，例如原始训练集的10％足以让我们的方法与1到10次的完全注释的数据库达到类似的性能。我们的代码和训练有素的模型公开可用
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process. In this work, we relax the labelling requirement by combining self-supervised with semi-supervised learning. We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images, which is combined with the supervised training of a small number of labelled images for learning the segmentation task. In our experiments, we evaluate on a few-shot microscopy image cell segmentation benchmark and show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots. Our code and trained models is made publicly available
