Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping

Autor:	Nishimura, Kazuya, Katanaya, Ami, Chuma, Shinichiro, Bise, Ryoma
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 9figures, MICCAI 2023 accepted
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.04113 Zobrazit plný text záznamu View this record from Arxiv