Page 228 - 《软件学报》2021年第5期
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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.