ArticleFrontiers in artificial intelligence2025
Early-fusion hybrid CNN-transformer models for multiclass ovarian tumor ultrasound classification.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Comparative assessment of DeepSeek-R1 and GPT-4 for structured ultrasound reporting of adnexal masses.Frontiers in oncology · 2026Article
- SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.Frontiers in medicine · 2026Article
- Robust benchmarking of DeepLabv3 hybrid models for multiclass fundus screening and referable-disease triage on the FIVES dataset.Frontiers in medicine · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
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Authors and funding
4 authors.
Funding
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Abstract
Ovarian cancer remains the deadliest gynecologic malignancy, and transvaginal ultrasound (TVS), the first-line test, still suffers from limited specificity and operator dependence. We introduce a learned early-fusion (joint projection) hybrid that couples EfficientNet-B7 (local descriptors) with a Swin Transformer (hierarchical global context) to classify eight ovarian tumor categories from 2D TVS. Using the public, de-identified OTU-2D dataset (
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Registered trials
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