Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.

Photiou C, Cloconi C, Strouthos I

DOI: 10.1007/s10278-024-01241-4
Published: 2025 Apr
Container: Journal of imaging informatics in medicine
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1007/s10278-024-01241-4,
  title = {Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.},
  author = {Photiou C and Cloconi C and Strouthos I},
  year = {2025},
  journal = {Journal of imaging informatics in medicine},
  doi = {10.1007/s10278-024-01241-4},
  url = {https://doi.org/10.1007/s10278-024-01241-4}
}

RIS

TY  - JOUR
TI  - Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.
AU  - Photiou C
AU  - Cloconi C
AU  - Strouthos I
PY  - 2025
JO  - Journal of imaging informatics in medicine
DO  - 10.1007/s10278-024-01241-4
UR  - https://doi.org/10.1007/s10278-024-01241-4
ER  -

APA

C, P., C, C., & I, S. (2025). Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.. Journal of imaging informatics in medicine. https://doi.org/10.1007/s10278-024-01241-4

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pubmed · retrieved 2026-09-25T05:48:21.898Z