Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amount of labeled data. However, these methods require annotations for each imaging condition. Collecting labeled data involves time-consuming human labor. In this paper, we propose a mitosis detection method that can be trained with partially annotated sequences. The base idea is to generate a fully labeled dataset from the partial labels and train a mitosis detection model with the generated dataset. First, we generate an image pair not containing mitosis events by frame-order flipping. Then, we paste mitosis events to the image pair by alpha-blending pasting and generate a fully labeled dataset. We demonstrate the performance of our method on four datasets, and we confirm that our method outperforms other comparisons which use partially labeled sequences.
@article{arxiv.2307.04113,
title = {Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping},
author = {Kazuya Nishimura and Ami Katanaya and Shinichiro Chuma and Ryoma Bise},
journal= {arXiv preprint arXiv:2307.04113},
year = {2023}
}