A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

Antropova, Natalia, Huynh, Benjamin Q., Giger, Maryellen L.·2017·Medical Physics

Cited by (389)

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  76. From pretraining to privacy: federated ultrasound foundation model with self-supervised learning
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  78. A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma
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  87. Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image
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  90. Analysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques
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  92. Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application
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  96. A critical assessment of artificial intelligence in magnetic resonance imaging of cancer
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