{
    "abstract": "This study focuses on breast imaging. A spectral approach based on the Fourier diffraction theorem is combined with a pair of U-NETs to perform real-time quantitative human breast imaging. The U-NET pair is trained based on the input of an induced current spectrum and the output of a contrast dielectric spectrum. A spectral database is constructed using combinations of anthropomorphic cavities. The weighted mean absolute percentage error (WMAPE) loss is associated with the Adam optimizer to perform optimization. Numerical results are presented to validate the proposed concept to demonstrate the transformation brought about by the U-NETs.",
    "authors": [
        {
            "affiliation": "Sorbonne Universit\u00e9, CNRS, Laboratoire de G\u00e9nie Electrique et Electronique de Paris, 75252 Paris, France.",
            "firstname": "Ambroise",
            "initials": "A",
            "lastname": "Di\u00e8s"
        },
        {
            "affiliation": "Sorbonne Universit\u00e9, CNRS, Laboratoire de G\u00e9nie Electrique et Electronique de Paris, 75252 Paris, France.",
            "firstname": "H\u00e9l\u00e8ne",
            "initials": "H",
            "lastname": "Roussel"
        },
        {
            "affiliation": "Sorbonne Universit\u00e9, CNRS, Laboratoire de G\u00e9nie Electrique et Electronique de Paris, 75252 Paris, France.\nUniversit\u00e9 Paris Cit\u00e9, F-75006 Paris, France.",
            "firstname": "Nadine",
            "initials": "N",
            "lastname": "Joachimowicz"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3390/s25082450\n10.1088/0266-5611/13/6/013\n10.1109/JERM.2024.3414196\n10.1109/42.56334\n10.1109/TAP.2017.2751668\n10.1109/8.121595\n10.1109/JERM.2024.3411799\n10.1109/TAP.2018.2885437\n10.1109/JSEN.2020.3030321\n10.1109/TAP.2022.3162320\n10.1109/TAP.2022.3216999\n10.1109/JMMCT.2021.3076827\n10.1109/LAWP.2012.2236293\n10.1038/sdata.2018.257\n10.3390/bioengineering9110651\n10.1109/ACCESS.2023.3291076\n10.3390/jimaging6080080\n10.3390/bioengineering10101153\n10.1109/MetroXRAINE62247.2024.10796628\n10.1002/ima.1850020210\n10.3390/rs14040900\n10.3390/diagnostics8040085\n10.1109/CAMA57522.2023.10352905\n10.3390/s24206663\n10.1109/TMI.2002.800590\n10.1109/MAP.2017.2731199\n10.1177/016173468200400404\n10.1137/24M167370X\n10.48550/arXiv.1912.01703\n10.1109/TIP.2003.819861\n10.21105/joss.04101",
    "journal": "Sensors (Basel, Switzerland)",
    "keywords": [
        "U-NET",
        "anthropomorphic breast model",
        "backpropagation",
        "deep learning",
        "microwave imaging",
        "spectral techniques"
    ],
    "methods": null,
    "publication_date": "2025-04-26",
    "pubmed_id": "40285140\n18222767\n25132808\n30457568\n36354562\n34460695\n37892883\n30567344\n39460143\n12166868\n6891131\n15376593",
    "results": null,
    "title": "Application of Spectral Approach Combined with U-NETs for Quantitative Microwave Breast Imaging.",
    "xml": "<Element 'PubmedArticle' at 0x78850656ffb0>"
}