GFDet: Multi-Level Feature Fusion Network for Caries Detection Using Dental Endoscope Images Article Swipe
Nan Gao
,
Yukai Li
,
Peng Chen
,
Jijun Tang
,
Tianshuang Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26599/bdma.2024.9020027
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26599/bdma.2024.9020027
Early dental caries detection by endoscope can prevent complications, such as pulpitis and apical infection. However, automatically identifying dental caries remains challenging due to the uncertainty in size, contrast, low saliency, and high interclass similarity of dental caries. To address these problems, we propose the Global Feature Detector (GFDet) that integrates the proposed Feature Selection Pyramid Network (FSPN) and Adaptive Assignment-Balanced Mechanism (AABM). Specifically, FSPN performs upsampling with the semantic information of adjacent feature layers to mitigate the semantic information loss due to sharp channel reduction and enhance discriminative features by aggregating fine-grained details and high-level semantics. In addition, a new label assignment mechanism is proposed that enables the model to select more high-quality samples as positive samples, which can address the problem of easily ignored small objects. Meanwhile, we have built an endoscopic dataset for caries detection, consisting of 1318 images labeled by five dentists. For experiments on the collected dataset, the F1-score of our model is 75.6%, which out-performances the state-of-the-art models by 7.1%.
Related Topics
Concepts
Discriminative model
Artificial intelligence
Computer science
Upsampling
Feature (linguistics)
Pyramid (geometry)
Pattern recognition (psychology)
Pooling
Feature selection
Semantics (computer science)
Convolutional neural network
Computer vision
Image (mathematics)
Mathematics
Geometry
Programming language
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/bdma.2024.9020027
- OA Status
- diamond
- Cited By
- 1
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405022143
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4405022143Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26599/bdma.2024.9020027Digital Object Identifier
- Title
-
GFDet: Multi-Level Feature Fusion Network for Caries Detection Using Dental Endoscope ImagesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-01Full publication date if available
- Authors
-
Nan Gao, Yukai Li, Peng Chen, Jijun Tang, Tianshuang LiuList of authors in order
- Landing page
-
https://doi.org/10.26599/bdma.2024.9020027Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.26599/bdma.2024.9020027Direct OA link when available
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Discriminative model, Artificial intelligence, Computer science, Upsampling, Feature (linguistics), Pyramid (geometry), Pattern recognition (psychology), Pooling, Feature selection, Semantics (computer science), Convolutional neural network, Computer vision, Image (mathematics), Mathematics, Geometry, Programming language, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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