Cited by 27AI in cancer detectionDigital Radiography and Breast ImagingRadiomics and Machine Learning in Medical Imaging

Improving Breast Cancer Detection Using Symmetry Information with Deep Learning

Open access
Brhane Hagos, Yeman, Gubern Mérida, Albert, Teuwen, Jonas·2018·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-030-00946-5_10

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

2 of the 15 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Brhane Hagos, Y., Gubern Mérida, A., & Teuwen, J.
Affiliations
Radboud University Medical Center (NL)
Venue
Lecture Notes in Computer Science
Published
2018
Pages
90-97
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2888567579
Cited by
27
References
15
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 7 · 2020: 6 · 2019: 1
FWCI
1.42
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
breast cancer detection, mammography, computer-aided detection, breast mass detection, temporal data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review