Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts
By integrating specialized algorithms for calcifications and masses, the MoRFSE model effectively emulates radiologists' diagnostic process. These findings suggest that MoRFSE has the potential to improve the accuracy of breast cancer diagnosis based on mammograms.
Authors
- Keisuke Sugawara
- Tomoya Kobayashi (ORCID: https://orcid.org/0000-0001-8179-1702)
- Yoshikazu Okamoto
- Takuya Ueda (ORCID: https://orcid.org/0000-0002-0913-5791)
- Eichi Takaya (ORCID: https://orcid.org/0000-0003-2541-1685)
- Ryusei Inamori (ORCID: https://orcid.org/0009-0005-4458-6044)
- Yuta Shiratori
- Jumpei Sato
- Y Konaka
- Fumihito Hario
Institutions
- Tohoku University (JP)
- Tohoku University Hospital (JP)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1371/journal.pone.0331017
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Core Research for Evolutional Science and Technology