BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Chen et al. integrated semantic trees into BERT to construct an interpretable neural network.
“Chen et al. [104] took the capabilities of BERT further by integrating semantic trees into the model, thus constructing an interpretable neural network.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [104]
Chen et al. [104] achieved Mi-P, Mi-R, and Mi-F1 of 91.58% and Ma-P 75.95%, Ma-R 79.73%, and Ma-F1 77.14%.
“| Chen et al. [104] | Word embedding, syntactic structure | Text | Shanghai Ruijin Hospital Molybdenum Mammography X-ray Report | Mi-P(%) = 91.58 Mi-R(%) = 91.58 Mi-F1(%) = 91.58 Ma-P(%) = 75.95 Ma-R(%) = 79.73 Ma-F1(%) = 77.14 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [104]
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References 27 · Cited by 3
- Authors
- Chen, D., Zhong, K., & He, J.
- Affiliations
- Donghua University (CN)
- Ruijin Hospital (CN)
- Corresponding author
- Chen, Dehua
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 419-433
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3190702023
- Cited by
- 3
- References
- 27
- Citations by year
- 2024: 1 · 2023: 2
- FWCI
- 0.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- breast cancer diagnosis, capsule networks, semantic embeddings, explainable AI, interpretability, BERT, attention mechanism, dynamic routing, breast tumor classification, text classification, medical image classification
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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.
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