ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2020
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
- An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.Nature communications · 2026Article
- Review
- Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound.Medical physics · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Comparison of Radiomics and Deep Learning Using Intestinal Ultrasound Images for Differentiating Inflammatory and Fibrotic Intestinal Strictures in Crohn's Disease.Journal of inflammation research · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma.NPJ precision oncology · 2024Article
- Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas.Scientific reports · 2024Article
- EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.Journal of imaging informatics in medicine · 2024Article
- Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.Frontiers in oncology · 2024Article
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
- Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI.Radiology · 2023Article
- Predicting breast cancer types on and beyond molecular level in a multi-modal fashion.NPJ breast cancer · 2023Article
- Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer.Scientific reports · 2023Article
- Criteria for the translation of radiomics into clinically useful tests.Nature reviews. Clinical oncology · 2023Review
- Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes.Frontiers in oncology · 2023Article
- Performance metric curve analysis framework to assess impact of the decision variable threshold, disease prevalence, and dataset variability in two-class classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Preoperative prediction of lymph node metastasis using deep learning-based features.Visual computing for industry, biomedicine, and art · 2022Article
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5 authors.
Funding
Abstract
Digital image-based signatures of breast tumors may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond traditional human-engineered computer vision methods, tumor classification methods using transfer learning from deep convolutional neural networks (CNNs) are actively under development. This article will first discuss our progress in using CNN-based transfer learning to characterize breast tumors for various diagnostic, prognostic, or predictive image-based tasks across multiple imaging modalities, including mammography, digital breast tomosynthesis, ultrasound (US), and magnetic resonance imaging (MRI), compared to both human-engineered feature-based radiomics and fusion classifiers created through combination of such features. Second, a new study is presented that reports on a comprehensive comparison of the classification performances of features derived from human-engineered radiomic features, CNN transfer learning, and fusion classifiers for breast lesions imaged with MRI. These studies demonstrate the utility of transfer learning for computer-aided diagnosis and highlight the synergistic improvement in classification performance using fusion classifiers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.