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
翻译:深神经网络目前为显微镜图像细胞分离提供有希望的结果,但是它们需要大规模贴标签的数据库,这是一个昂贵和耗时的过程。在这项工作中,我们放松标签要求,将自我监督与半监督学习相结合。我们提议预测以边缘为基础的地图进行无标签图像培训的自我监督,同时对少数贴标签的图像进行监督培训,以了解分化任务。在我们的实验中,我们评估了微小的显微镜图像细胞分解基准,并表明只有少量附加说明的图像,例如,原始培训成套材料的10%,足以使我们达到与1至10发全注数据库类似的效果。我们的代码和经过培训的模型被公诸于众。