Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound

Nicholas Dietrich, David McShannon

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DOI: 10.1371/journal.pdig.0001797
Published: 2026-11-04
Container: PLOS Digital Health
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pdig.0001797,
  title = {Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound},
  author = {Nicholas Dietrich and David McShannon},
  year = {2026},
  journal = {PLOS Digital Health},
  doi = {10.1371/journal.pdig.0001797},
  url = {https://doi.org/10.1371/journal.pdig.0001797}
}

RIS

TY  - JOUR
TI  - Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound
AU  - Nicholas Dietrich
AU  - David McShannon
PY  - 2026
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001797
UR  - https://doi.org/10.1371/journal.pdig.0001797
ER  -

APA

Dietrich, N., & McShannon, D. (2026). Adversarial robustness of deep learning-based thyroid nodule segmentation in ultrasound. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0001797

Source records

crossref · retrieved 2026-11-07T20:00:44.050Z