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zadetkov: 9.368
1.
  • Automated detection of pulm... Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique
    Teramoto, Atsushi; Fujita, Hiroshi; Yamamuro, Osamu ... Medical physics (Lancaster), June 2016, Letnik: 43, Številka: 6
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    Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect ...
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2.
  • Deep learning of the sectio... Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method
    Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song ... Medical physics (Lancaster), October 2017, Letnik: 44, Številka: 10
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    Purpose We propose a single network trained by pixel‐to‐label deep learning to address the general issue of automatic multiple organ segmentation in three‐dimensional (3D) computed tomography (CT) ...
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3.
  • Automated Classification of... Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks
    Teramoto, Atsushi; Tsukamoto, Tetsuya; Kiriyama, Yuka ... BioMed research international, 01/2017, Letnik: 2017
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    Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small ...
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4.
  • Classification of teeth in ... Classification of teeth in cone-beam CT using deep convolutional neural network
    Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro ... Computers in biology and medicine, 01/2017, Letnik: 80
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    Abstract Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of ...
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5.
  • Evaluation of an artificial... Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
    Fukuda, Motoki; Inamoto, Kyoko; Shibata, Naoki ... Oral radiology, 10/2020, Letnik: 36, Številka: 4
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    Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred ...
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6.
  • Deep-learning classificatio... Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography
    Murata, Makoto; Ariji, Yoshiko; Ohashi, Yasufumi ... Oral radiology, 09/2019, Letnik: 35, Številka: 3
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    Objectives To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. Methods Training data for 400 healthy and 400 ...
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7.
  • Deep learning approach to c... Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks
    Teramoto, Atsushi; Tsukamoto, Tetsuya; Yamada, Ayumi ... PloS one, 03/2020, Letnik: 15, Številka: 3
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    Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify ...
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8.
  • A deep-learning artificial ... A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography
    Hiraiwa, Teruhiko; Ariji, Yoshiko; Fukuda, Motoki ... Dento-maxillo-facial radiology 48, Številka: 3
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    The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a ...
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9.
  • Lung cancer CT image genera... Lung cancer CT image generation from a free-form sketch using style-based pix2pix for data augmentation
    Toda, Ryo; Teramoto, Atsushi; Kondo, Masashi ... Scientific reports, 07/2022, Letnik: 12, Številka: 1
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    Abstract Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data ...
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  • Breast mass classification ... Breast mass classification on mammograms using radial local ternary patterns
    Muramatsu, Chisako; Hara, Takeshi; Endo, Tokiko ... Computers in biology and medicine, 05/2016, Letnik: 72
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    Abstract Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin ...
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zadetkov: 9.368

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