Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification Article Swipe
Zhiwei Wang
,
Junlin Xian
,
Kangyi Liu
,
Xin Li
,
Qiang Li
,
Xin Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/168
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/168
Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information for clinical decisions. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation in the feature learning. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep feature maps for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated with each other. Experimental results on the two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that the DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms previous state-of-the-art methods for classifying a whole mammogram as malignant or not.
Related Topics
Concepts
Feature (linguistics)
Correlation
Artificial intelligence
Dual (grammatical number)
Similarity (geometry)
Pattern recognition (psychology)
Computer science
Maximization
Relation (database)
Machine learning
Mathematics
Image (mathematics)
Data mining
Mathematical optimization
Philosophy
Linguistics
Art
Geometry
Literature
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2023/168
- https://www.ijcai.org/proceedings/2023/0168.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385764398
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385764398Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2023/168Digital Object Identifier
- Title
-
Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Zhiwei Wang, Junlin Xian, Kangyi Liu, Xin Li, Qiang Li, Xin YangList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2023/168Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2023/0168.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.ijcai.org/proceedings/2023/0168.pdfDirect OA link when available
- Concepts
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Feature (linguistics), Correlation, Artificial intelligence, Dual (grammatical number), Similarity (geometry), Pattern recognition (psychology), Computer science, Maximization, Relation (database), Machine learning, Mathematics, Image (mathematics), Data mining, Mathematical optimization, Philosophy, Linguistics, Art, Geometry, LiteratureTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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