Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification

Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura, Ryoma Bise

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Cell detection is an essential task in cell image analysis. Recent deep learning-based detection methods have achieved very promising results. In general, these methods require exhaustively annotating the cells in an entire image. If some of the cells are not annotated (imperfect annotation), the detection performance significantly degrades due to noisy labels. This often occurs in real collaborations with biologists and even in public data-sets. Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data. A detection convolutional neural network (CNN) trained using such missing labeled data often produces over-detection. We treat partially labeled cells as positive samples and the detected positions except for the labeled cell as unlabeled samples. Then we select reliable pseudo labels from unlabeled data using recent machine learning techniques; positive-and-unlabeled (PU) learning and P-classification. Experiments using microscopy images for five different conditions demonstrate the effectiveness of the proposed method. Our code is available at https://github.com/FujiiKazuma/CDFIAPLSUP.git.

本文言語英語
ホスト出版物のタイトルMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
編集者Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
出版社Springer Science and Business Media Deutschland GmbH
ページ425-434
ページ数10
ISBN(印刷版)9783030872366
DOI
出版ステータス出版済み - 2021
イベント24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
継続期間: 9 27 202110 1 2021

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12908 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period9/27/2110/1/21

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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