Semantic segmentation dataset authoring with simplified labels
Simplified labels offer a framework that can be applied both to dataset authoring and to multi-dataset training. Using the proposed method, non-medical annotators can participate in semantic segmentation dataset authoring. Labels of incompatible datasets can be converted into simplified datasets, enabling multi-dataset training.
Authors
- Takayuki Kitasaka (ORCID: https://orcid.org/0000-0003-1648-1999)
- Kensaku Mori (ORCID: https://orcid.org/0000-0002-0100-4797)
- Leo Uramoto (ORCID: https://orcid.org/0009-0006-8795-7906)
- Yuichiro Hayashi (ORCID: https://orcid.org/0000-0001-5241-8669)
- Masahiro Oda (ORCID: https://orcid.org/0000-0001-7714-422X)
Institutions
- National Institute of Informatics (JP)
- Aichi Institute of Technology (JP)
- Nagoya University (JP)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1007/s11548-024-03314-9
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science
- Japan Science and Technology Agency
- Core Research for Evolutional Science and Technology