Publication record · 2026
Diagnostic Driven Topology Adaptive Generative Adversarial Networks for Improved Breast Cancer Diagnosis
- Authors
- Year
- 2026
- Publisher
- Springer Nature
- Journal or publication
- Archives of Computational Methods in Engineering
- Google Scholar citations
- 1
Journal indicators
CiteScore 2025 (Scopus)34.8
Best quartileQ1
Top 10% in any categoryYes
Positions by category
- Applied Mathematics2/680 · Q1 · percentile 99 · Top 10%
- Computer Science Applications7/1022 · Q1 · percentile 99 · Top 10%
JCR (Clarivate): Not verified: the supplied ranking file is Scopus CiteScore, not JCR.
Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).
The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.Research summary
DTA-GAN adapts its generator and discriminator topology during training using diagnostic criteria relevant to mammography. Evaluation on CBIS-DDSM, INbreast and Mini-MIAS studies image fidelity, preservation of lesion features and downstream breast-cancer classification.
Original description prepared for this website; consult the publication for its authoritative abstract.DOI 10.1007/s11831-025-10430-5 ↗ · Repository record ↗ · View Google Scholar record ↗