  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
