ArticleJournal of imaging2020
Full 3D Microwave Breast Imaging Using a Deep-Learning Technique.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Microwave breast imaging: A review of clinical potential and technological advances.World journal of methodology · 2026Review
- Application of Spectral Approach Combined with U-NETs for Quantitative Microwave Breast Imaging.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning in Microwave Medical Imaging and Lesion Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence.Npj imaging · 2024Review
- Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing.Sensors (Basel, Switzerland) · 2023Review
- A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography.Diagnostics (Basel, Switzerland) · 2023Article
- Radar-Based Microwave Breast Imaging Using Neurocomputational Models.Diagnostics (Basel, Switzerland) · 2023Article
- Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models.Biosensors · 2023Article
- A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.Biosensors · 2023Article
- Holographic Microwave Image Classification Using a Convolutional Neural Network.Micromachines · 2022Article
- An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications.Bioengineering (Basel, Switzerland) · 2022Article
- Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions.Journal of imaging · 2022Review
- A deep learning model to classify and detect brain abnormalities in portable microwave based imaging system.Scientific reports · 2022Article
- Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning in Medical Image Analysis.Journal of imaging · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A deep learning technique to enhance 3D images of the complex-valued permittivity of the breast obtained via microwave imaging is investigated. The developed technique is an extension of one created to enhance 2D images. We employ a 3D Convolutional Neural Network, based on the U-Net architecture, that takes in 3D images obtained using the Contrast-Source Inversion (CSI) method and attempts to produce the true 3D image of the permittivity. The training set consists of 3D CSI images, along with the true numerical phantom images from which the microwave scattered field utilized to create the CSI reconstructions was synthetically generated. Each numerical phantom varies with respect to the size, number, and location of tumors within the fibroglandular region. The reconstructed permittivity images produced by the proposed 3D U-Net show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but it also enhances the detectability of the tumors. We test the trained U-Net with 3D images obtained from experimentally collected microwave data as well as with images obtained synthetically. Significantly, the results illustrate that although the network was trained using only images obtained from synthetic data, it performed well with images obtained from both synthetic and experimental data. Quantitative evaluations are reported using Receiver Operating Characteristics (ROC) curves for the tumor detectability and RMS error for the enhancement of the reconstructions.
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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.