Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos
This study introduces a novel heterogeneous transfer learning approach, ASFR, which significantly enhances the precision of vascular segmentation in laparoscopic videos. ASFR effectively addresses critical challenges in surgical image analysis and paves the way for broader applications in laparoscopic surgery, promising improved patient outcomes and increased surgical efficiency.
Authors
- Takayuki Kitasaka (ORCID: https://orcid.org/0000-0003-1648-1999)
- Kensaku Mori (ORCID: https://orcid.org/0000-0002-0100-4797)
- Masahiro Oda (ORCID: https://orcid.org/0000-0001-7714-422X)
- Yuichiro Hayashi
- Xinkai Zhao
Institutions
- National Institute of Informatics (JP)
- Aichi Institute of Technology (JP)
- Aichi Cancer Center (JP)
- Nagoya University (JP)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1007/s11548-025-03404-2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Nagoya University
- Japan Society for the Promotion of Science
- Japan Science and Technology Agency
- Core Research for Evolutional Science and Technology