Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology
This addresses the challenge of reducing annotation effort for cell tracking in medical imaging, though it is incremental as it builds on existing weakly-supervised approaches.
The paper tackles the problem of cell tracking in pathology by proposing a weakly-supervised method that uses only cell detection annotations without association information, achieving performance almost equal to state-of-the-art supervised methods.
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 a co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that the co-detection CNN implicitly learns association in addition to detection. To obtain the association information, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the detection maps output of the co-detection CNN. Experiments demonstrated that the proposed method can match positions by analyzing the 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.