ArticleCancers2022
Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.Breast cancer research : BCR · 2025Article
- Breast cancer classification based on hybrid CNN with LSTM model.Scientific reports · 2025Article
- Innovative breast cancer detection using a segmentation-guided ensemble classification framework.Biomedical engineering letters · 2025Article
- Measuring Subtle HD Data Representation and Multimodal Imaging Phenotype Embedding for Precision Medicine.IEEE transactions on instrumentation and measurement · 2025Article
- Article
- Real-time AI-guided ultrasound localization method for breast tumor rotational resection.Frontiers in oncology · 2025Article
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.Journal of imaging informatics in medicine · 2024Article
- Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images.Visual computing for industry, biomedicine, and art · 2024Article
- Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study.Radiology. Artificial intelligence · 2023Article
- Dynamic Vascular Imaging Using Active Breast Thermography.Sensors (Basel, Switzerland) · 2023Article
- A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images-437 benign, 210, malignant, and 133 normal-were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1-84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.
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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.