Bidirectional teaching between lightweight multi-view networks for intestine segmentation from CT volume
The proposed method can effectively utilize large-scale unlabeled data with pseudo-labels, which is crucial in reducing the effect of limited labeled data in medical image segmentation. Furthermore, we assign different weights to the pseudo-labels to improve their reliability. From the result, we can see that the method produced competitive performance compared with previous methods.
Authors
- Akinari Hinoki (ORCID: https://orcid.org/0000-0001-6878-2043)
- Takayuki Kitasaka (ORCID: https://orcid.org/0000-0003-1648-1999)
- Hiroo Uchida (ORCID: https://orcid.org/0000-0002-1818-6697)
- An Qin (ORCID: https://orcid.org/0000-0002-3333-5363)
- Kensaku Mori (ORCID: https://orcid.org/0000-0002-0100-4797)
- Yuichiro Hayashi (ORCID: https://orcid.org/0000-0001-5241-8669)
- Kojiro Suzuki (ORCID: https://orcid.org/0000-0002-8403-9425)
- Aitaro Takimoto (ORCID: https://orcid.org/0000-0002-8744-2949)
- Masahiro Oda (ORCID: https://orcid.org/0000-0001-7714-422X)
- Hirohisa Oda (ORCID: https://orcid.org/0000-0003-0896-4333)
Institutions
- University of Shizuoka (JP)
- Aichi Medical University (JP)
- National Institute of Informatics (JP)
- Aichi Institute of Technology (JP)
- Nagoya University (JP)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1117/1.jmi.12.2.024003
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00