论文标题
通过向后传播的弱监督细胞跟踪
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
论文作者
论文摘要
我们提出了一种弱监督的细胞跟踪方法，该方法可以通过仅使用“细胞检测”（即细胞位置的坐标）的注释来训练卷积神经网络（CNN），而无需关联信息，其中可以通过核染色轻松获得细胞位置。首先，我们训练通过使用弱标签来检测连续帧中细胞的共同检测CNN。我们的关键假设是，除了检测外，共同检测CNN隐含地学习了关联。为了获得关联，我们提出了一种向后传播方法，该方法分析了共同检测CNN输出中细胞位置的对应关系。实验表明，该提出的方法可以通过分析共检测CNN将细胞关联。即使该方法仅使用弱监督，我们的方法的性能几乎与最先进的监督方法相同。代码可在https://github.com/naivete5656/wsctbfp中公开使用
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that co-detection CNN implicitly learns association in addition to detection. To obtain the association, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in https://github.com/naivete5656/WSCTBFP
