Publication2024
CAGCL: Predicting Short- and Long-Term Breast Cancer Survival With Cross-Modal Attention and Graph Contrastive Learning
- Good Health and Well-being
- Industry, Innovation and Infrastructure
- Empowered Indians
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- IEEE Journal of Biomedical and Health Informatics
- Volume
- 28
- Issue
- 12
- Pages
- 7382-7391
- Published
- 1 January 2024
- DOI
- 10.1109/jbhi.2024.3449756
- Scopus ID
- 85203513655
- ISSN
- 21682194
- Authors
- 4
- Open access
- No
- Citations
- 8
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
Cross-modal deep learning model integrating multi-omics data achieves 0.948 AUC for accurate breast cancer survival prediction, directly improving treatment planning and patient outcomes.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Graph contrastive learning and multi-modal attention mechanisms represent advanced machine learning research for medical data integration and analysis.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Cross-modal deep learning model integrating multi-omics data achieves 0.948 AUC for accurate breast cancer survival prediction, directly improving treatment planning and patient outcomes.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “CAGCL: Predicting Short- and Long-Term Breast Cancer Survival With Cross-Modal Attention and Graph Contrastive Learning”. Kortex.