Page 228 - 《软件学报》2021年第5期
P. 228

1452                                     Journal of Software  软件学报 Vol.32, No.5,  May 2021

                 [32]    Bándi  P, Geessink  O, Manson Q,  van  Dijk  M, Balkenhol  M, Hermsen M, Bejnordi BE, Lee B,  Paeng K, Zhong AX,  Li QZ,
                      Zanjani FG, Zinger S, Fukuta K, Komura D, Ovtcharov V, Cheng SH, Zeng SQ, Thagaard J, Dahl AB, Lin HJ, Chen H, Jacobsson
                      L, Hedlund M, Çetin M, Halıcı E, Jackson H, Chen R, Both F, Franke J, Küsters-Vandevelde H, Vreuls W, Bult P, van Ginneken
                      B, van der Laak J, Litjens G. From detection of individual metastases to classification of lymph node status at the patient level:
                      The CAMELYON17 challenge. IEEE Trans. on Medical Imaging, 2019,38(2):550−560.
                 [33]    Lai MD. Histologic Atlas Grading of Common Tumor. Beijing: People’s Medical Publishing House, 2009 (in Chinese).
                 [34]    Irshad H, Montaser-Kouhsari L, Waltz G, Bucur O, Nowak  JA,  Dong F, Knoblauch NW, Beck AH. Crowdsourcing image
                      annotation for nucleus detection and segmentation in computational pathology: Evaluating experts, automated methods, and the
                      crowd. In: Proc. of the Pacific Symp. on Biocomputing. 2015. 294−305.
                 [35]    Kumar MD, Babaie M, Zhu  SJ, Kalra  S, Tizhoosh HR. A comparative  study  of CNN, BoVW and LBP  for  classification  of
                      histopathological images. In: Proc. of the IEEE Symp. Series on Computational Intelligence. 2017. 1−7.
                 [36]    Gelasca ED, Byun JY, Obara B, Manjunath BS. Evaluation and benchmark for biological image segmentation. In: Proc. of the
                      IEEE Int’l Conf. on Image Processing. 2008. 1816−1819.
                 [37]    Brown KM, Barrionuevo G, Canty AJ, De  Paola V, Hirsch  JA,  Jefferis  GSXE, Lu  J,  Snippe M,  Sugihara  I, Ascoli GA.  The
                      DIADEM data sets:  Representative light  microscopy images of neuronal  morphology to advance  automation of digital
                      reconstructions. Neuroinformatics, 2011,9:143−157.
                 [38]    Kather JN, Marx A, Reyes-Aldasoro  CC,  Schad LR, Zöllner  FG,  Weis CA. Continuous representation  of  tumor microvessel
                      density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget, 2015,6(22):19163−19176.
                 [39]    Li C, Wang XG, Liu WY, Latecki LJ, Wang B, Huang JZ. Weakly supervised mitosis detection in breast histopathology images
                      using concentric loss. Medical Image Analysis, 2019,53:165−178.
                 [40]    Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A. A dataset and a technique for generalized nuclear segmentation
                      for computational pathology. IEEE Trans. on Medical Imaging, 2017,36(7):1550−1560.
                 [41]    Kumar N, Verma R, Anand D, Zhou YN, Onder OF, Tsougenis E, Chen H, Heng PA, Li JH, Hu ZQ, Wang YZ, Koohbanani NA,
                      Jahanifar M, Tajeddin NZ, Gooya A, Rajpoot N, Ren XH, Zhou SH, Wang Q, Shen DG, Yang CK, Weng CH, Yu WH, Yeh CY,
                      Yang S, Xu SY, Yeung PH, Sun P, Mahbod A, Schaefer G, Ellinger R, Ecker O, Smedby CL, Wang B, Chidester TV, Ton MT,
                      Tran J, Ma MN, Do S, Graham QD, Vu JT, Kwak A, Gunda R, Chunduri I, Hu C, Zhou XY, Lotfi D, Safdari R, Kascenas A,
                      O’Neil A, Eschweiler D, Stegmaier J, Cui YP, Yin BC, Chen KL, Tian XM, Gruening P, Barth E, Arbel E, Remer I, Ben-Dor A,
                      Sirazitdinova E, Kohl M, Braunewell S, Li YX, Xie XP, Shen LL, Ma J, Baksi KD, Khan MA, Choo J, Colomer A, Naranjo V,
                      Pei LM, Iftekharuddin KM, Roy K, Bhattacharjee D, Pedraza A, Bueno MG, Devanathan S, Radhakrishnan S, Koduganty P, Wu
                      ZH, Cai GY, Liu XJ, Wang YQ, Sethi A. A multi-organ nucleus segmentation challenge. IEEE Trans. on Medical Imaging, 2020,
                      39(5):1380−1391.
                 [42]    Janowczyk A, Madabhushi  A.  Deep  learning for digital pathology image  analysis: A  comprehensive tutorial  with selected use
                      cases. Journal of Pathology Informatics, 2016,7:Article No.29.
                 [43]    Gertych A, Ing N, Ma ZX, Fuchs TJ, Salman S, Mohanty S, Bhele S, Velásquez-Vacca A, Amin MB, Knudsen BS. Machine
                      learning approaches to analyze histological images of tissues from radical prostatectomies. Computerized Medical Imaging and
                      Graphics, 2015,46:197−208.
                 [44]    Xu Y,  Jia  ZP,  Wang LB, Ai YQ, Zhang  F, Lai  MD, Chang EIC. Large  scale  tissue  histopathology  image classification,
                      segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics, 2017,18:Article No.281.
                 [45]    Veta M, Heng YJJ, Stathonikos N, Bejnordi BE, Beca F, Wollmann T, Rohr K, Shah MA, Wang DY, Rousson M, Hedlund M,
                      Tellez D, Ciompi F, Zerhouni E, Lanyi D, Viana M, Kovalev V, Liauchuk V, Phoulady HA, Qaiser T, Graham S, Rajpoot N,
                      Sjöblom E, Molin J, Paeng K, Hwang S, Park S, Jia ZP, Chang EIC, Xu Y, Beck AH, van Diest PJ, Pluim JPW. Predicting breast
                      tumor proliferation from whole-slide images: The TUPAC16 challenge. Medical Image Analysis, 2019,54:111−121.
                 [46]    Yan ZQ, Yang X, Cheng KT. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE
                      Trans. on Biomedical Engineering, 2018,65(9):1912−1923.
                 [47]    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015,521:436−444.
                 [48]    LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proc. of the IEEE Int’l Symp. on
                      Circuits and Systems. 2010. 253−256.
   223   224   225   226   227   228   229   230   231   232   233