دانلود مقاله انگلیسی رایگان:TOP-GAN: طبقه بندی سلول های سرطانی بدون لکه با استفاده از یادگیری عمیق با یک مجموعه آموزشی کوچک - 2019
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  • TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set
    TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set


    ترجمه فارسی عنوان مقاله:

    TOP-GAN: طبقه بندی سلول های سرطانی بدون لکه با استفاده از یادگیری عمیق با یک مجموعه آموزشی کوچک


    منبع:

    Sciencedirect - Elsevier - Medical Image Analysis, 57 (2019) 176-185: doi:10:1016/j:media:2019:06:014


    نویسنده:

    Moran Rubin a , b , Omer Stein b , Nir A. Turko a , Yoav Nygate a , Darina Roitshtain a , Lidor Karako a , Itay Barnea a , Raja Giryes b , NatanT. Shaked


    چکیده انگلیسی:

    We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and generative ad- versarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been im- aged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells are extracted and directly used as inputs to the networks. In order to cope with the small number of clas- sified images, we use GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, we change the last layers of the network and design automatic classifiers for the correct cell type (healthy/primary cancer/metastatic cancer) with 90–99% accuracies, although small training sets of down to several images are used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set.
    Keywords: Holography | Quantitative phase imaging | Deep learning | Machine learning algorithms | Image classification | Biological cells


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 1294 کیلوبایت

    قیمت: رایگان


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