Mitosis Detection from Partial Annotation by Dataset Generation via
  Frame-Order Flipping
Kazuya Nishimura Ami Katanaya Shinichiro Chuma Ryoma Bise
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.