Neural networks for computer-aided diagnosis in medicine: A review
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Bias correction and quantile-based intensity normalization are often successfully replaced by z-score computation alone in deep learning segmentation.
“For instance, bias correction and quantile-based intensity normalization are often successfully replaced by the z-score computation alone [2, 21];”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [2]
With advent of deep learning, some preprocessing steps became less critical for final segmentation performance.
“With advent of deep learning techniques, some of the preprocessing steps became less critical for the final segmentation performance.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [2]
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Deep learning for brain MRI segmentation: state of the art and future directions
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- Authors
- lin, D., Vasilakos, A. V., Tang, Y., & Yao, Y.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Luleå University of Technology (SE)
- Corresponding author
- lin, Di
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 216
- Pages
- 700-708
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2507931172
- Cited by
- 105
- References
- 128
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 12 · 2023: 9 · 2022: 18 · 2021: 15 · 2020: 17 · 2019: 8 · 2018: 13 · 2017: 6
- FWCI
- 15.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61370202)
- Fundamental Research Funds for the Central Universities (ZYGX2015J071)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial neural networks, computer-aided medical diagnosis, disease detection, disease classification, clinical decision support, medical image analysis
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.
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