SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.

Liu Q, Li C

DOI: 10.3389/fmed.2026.1942733
Published: 2026
Container: Frontiers in medicine
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fmed.2026.1942733,
  title = {SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.},
  author = {Liu Q and Li C},
  year = {2026},
  journal = {Frontiers in medicine},
  doi = {10.3389/fmed.2026.1942733},
  url = {https://doi.org/10.3389/fmed.2026.1942733}
}

RIS

TY  - JOUR
TI  - SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.
AU  - Liu Q
AU  - Li C
PY  - 2026
JO  - Frontiers in medicine
DO  - 10.3389/fmed.2026.1942733
UR  - https://doi.org/10.3389/fmed.2026.1942733
ER  -

APA

Q, L., & C, L. (2026). SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.. Frontiers in medicine. https://doi.org/10.3389/fmed.2026.1942733

Source records

pubmed · retrieved 2026-09-25T14:49:52.692Z