An integrated approach for medical abnormality detection using deep patch convolutional neural networks
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep learning algorithms have been developed to improve clinical accuracy and detect abnormalities in medical images, saving time and increasing dependability in patient care.
“In recent years, deep learning algorithms have been developed to save time and dependability during patient care by improving clinical accuracy and detecting abnormalities in medical images [104, 140].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [140]
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References 45 · Cited by 47
- Authors
- Xi, P., Guan, H., Shu, C., Borgeat, L., & Goubran, R.
- Affiliations
- National Research Council Canada (CA)
- Carleton University (CA)
- Corresponding author
- Xi, Pengcheng
- Guan, Haitao
- Venue
- The Visual Computer
- Published
- 2019-12-09
- Volume
- 36
- Issue
- 9
- Pages
- 1869-1882
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W2994072076
- Cited by
- 47
- References
- 45
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 11 · 2022: 12 · 2021: 8 · 2020: 6
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- NPARC (submittedVersion)
- Funders
- Foundational Research Fund of Application Key Technology, Nantong (MS12017010-6)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- computer-aided detection, patch-based convolutional neural network, class activation mapping, region proposal network, mammography, high-resolution medical images, small object detection
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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