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Volumn 9915 LNCS, Issue , 2016, Pages 532-539

Transfer learning for cell nuclei classification in histopathology images

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; IMAGE CLASSIFICATION; MEDICAL IMAGING;

EID: 85006040018     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-49409-8_46     Document Type: Conference Paper
Times cited : (80)

References (20)
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    • Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 411-418. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_51
    • (2013) MICCAI 2013. LNCS , vol.8149 , pp. 411-418
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  • 4
    • 84900449424 scopus 로고    scopus 로고
    • Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential
    • Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97-114 (2014)
    • (2014) IEEE Rev. Biomed. Eng , vol.7 , pp. 97-114
    • Irshad, H.1    Veillard, A.2    Roux, L.3    Racoceanu, D.4
  • 12
    • 84929483880 scopus 로고    scopus 로고
    • Transfer learning improves supervised image segmentation across imaging protocols
    • van Opbroek, A., Ikram, M.A., Vernooij, M.W., De Bruijne, M.: Transfer learning improves supervised image segmentation across imaging protocols. IEEE TMI 34(5), 1018-1030 (2015)
    • (2015) IEEE TMI , vol.34 , Issue.5 , pp. 1018-1030
    • Van Opbroek, A.1    Ikram, M.A.2    Vernooij, M.W.3    De Bruijne, M.4
  • 13
    • 84911449395 scopus 로고    scopus 로고
    • Learning and transferring mid-level image representations using convolutional neural networks
    • IEEE
    • Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: CVPR, pp. 1717-1724. IEEE (2014)
    • (2014) CVPR , pp. 1717-1724
    • Oquab, M.1    Bottou, L.2    Laptev, I.3    Sivic, J.4
  • 14
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    • Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
    • Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285-1298 (2016)
    • (2016) IEEE Trans. Med. Imaging , vol.35 , Issue.5 , pp. 1285-1298
    • Shin, H.C.1    Roth, H.R.2    Gao, M.3    Lu, L.4    Xu, Z.5    Nogues, I.6    Yao, J.7    Mollura, D.8    Summers, R.M.9
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    • Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
    • Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196-1206 (2016)
    • (2016) IEEE Trans. Med. Imaging , vol.35 , Issue.5 , pp. 1196-1206
    • Sirinukunwattana, K.1    Raza, S.E.A.2    Tsang, Y.W.3    Snead, D.R.4    Cree, I.A.5    Rajpoot, N.M.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.