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International Journal of Computer Assisted Radiology and Surgery

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01-02-2018 | Review Article

Breast cancer cell nuclei classification in histopathology images using deep neural networks

Authors: Yangqin Feng, Lei Zhang, Zhang Yi

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2018

Abstract

Purpose

Cell nuclei classification in breast cancer histopathology images plays an important role in effective diagnose since breast cancer can often be characterized by its expression in cell nuclei. However, due to the small and variant sizes of cell nuclei, and heavy noise in histopathology images, traditional machine learning methods cannot achieve desirable recognition accuracy. To address this challenge, this paper aims to present a novel deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner.

Methods

The proposed model hierarchically maps raw medical images into a latent space in which robustness is achieved by employing a stacked denoising autoencoder. A supervised classifier is further developed to improve the discrimination of the model by maximizing inter-subject separability in the latent space. The proposed method involves a cascade model which jointly learns a set of nonlinear mappings and a classifier from the given raw medical images. Such an on-the-shelf learning strategy makes obtaining discriminative features possible, thus leading to better recognition performance.

Results

Extensive experiments with benign and malignant breast cancer datasets are conducted to verify the effectiveness of the proposed method. Better performance was obtained when compared with other feature extraction methods, and higher recognition rate was achieved when compared with other seven classification methods.

Conclusions

We propose an end-to-end DNN model for cell nuclei and non-nuclei classification of histopathology images. It demonstrates that the proposed method can achieve promising performance in cell nuclei classification, and the proposed method is suitable for the cell nuclei classification task.

The BCC database is downloaded from the website at http://bioimage.ucsb.edu/research/bio-segmentation.

Basavanhally A, Xu J, Madabhushi A, Ganesan S (2009) Computer-aided prognosis of er+ breast cancer histopathology and correlating survival outcome with oncotype dx assay. In: IEEE international symposium on Biomedical imaging: from nano to macro (ISBI’09). IEEE pp 851–854

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

Chen X, Zhou X, Wong ST (2006) Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 53(4):762–766CrossRefPubMed

Cirean DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. Springer, Berlin, pp 411–418

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B 39(1):1–38

Fatakdawala H, Xu J, Basavanhally A, Bhanot G, Ganesan S, Feldman M, Tomaszewski JE, Madabhushi A (2010) Expectation maximization-driven geodesic active contour with overlap resolution (emagacor): application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng 57(7):1676–1689CrossRefPubMed

Fukunaga K, Hostetler LD (1975) K-nearest-neighbor bayes-risk estimation. IEEE Trans Inf Theory 21(3):285–293CrossRef

Gelasca E.D, Byun J, Obara B, Manjunath B (2008) Evaluation and benchmark for biological image segmentation. In: 15th IEEE International Conference on Image Processing (ICIP 2008). IEEE, pp 1816–1819

Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Aistats 9:249–256

10.

Hatipoglu N, Bilgin G (2014) Classification of histopathological images using convolutional neural network. In: 2014 4th international conference on image processing theory, tools and applications (IPTA). IEEE, pp 1–6

11.

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554CrossRefPubMed

12.

Huang PW, Lee CH (2009) Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imag 28(7):1037–1050CrossRef

13.

Kowal M (2014) Computer-aided diagnosis for breast tumor classification using microscopic images of fine needle biopsy. Springer, Berlin, pp 213–224

14.

Kullback S, Leibler RA (1951) On information and sufficiency. Anna Math Stat 22(1):79–86CrossRef

15.

Larochelle H, Erhan D, Courville A, Bergstra J, Bengio Y (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th international conference on Machine learning. ACM, pp 473–480

16.

LeCun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, Drucker H, Guyon I, Muller U, Sackinger E (1995) Comparison of learning algorithms for handwritten digit recognition. In: International conference on artificial neural networks, vol 60. pp 53–60

17.

Liang M, Li Z, Chen T, Zeng J (2015) Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinf 12(4):928–937. doi:10.1109/TCBB.2014.2377729 CrossRef

18.

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef

19.

McLachlan G (2004) Discriminant analysis and statistical pattern recognition. Wiley, Hoboken

20.

Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 5th IEEE international symposium on biomedical imaging: from nano to macro (ISBI 2008). IEEE, pp 284–287

21.

Ojansivu V, Linder N, Rahtu E, Pietikinen M, Lundin M, Joensuu H, Lundin J (2013) Automated classification of breast cancer morphology in histopathological images. Diagn Pathol 8(1):1–4

22.

Pang B, Zhang Y, Chen Q, Gao Z, Peng Q, You X Cell nucleus segmentation in color histopathological imagery using convolutional networks. In: 2010 Chinese conference on pattern recognition (CCPR). IEEE, pp 1–5

23.

Peng X, Yi Z, Tang H (2015) Robust subspace clustering via thresholding ridge regression. In: AAAI, pp 3827–3833

24.

Peng X, Zhao B, Yan R, Tang H, Yi Z (2016) Bag of events: an efficient probability-based feature extraction method for aer image sensors. IEEE Trans Neural Netw Learn Syst 99:1–13

25.

Poultney C, Chopra S, Cun Y.L (2007) Efficient learning of sparse representations with an energy-based model. In: Advances in neural information processing systems, pp 1137–1144

26.

Ranzato MA, Huang FJ, Boureau YL, LeCun Y (2007) Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: IEEE conference on computer vision and pattern recognition (CVPR’07). IEEE, pp 1–8

27.

Schlkopf B, Smola A, Mller KR (1997) Kernel principal component analysis. Springer, Berlin, pp 583–588

28.

Schmah T, Hinton G.E, Small S.L, Strother S, Zemel R.S (2009) Generative versus discriminative training of RBMS for classification of fmri images. In: Advances in neural information processing systems. pp 1409–1416

29.

Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, Cambridge

30.

Sertel O, Kong J, Shimada H, Catalyurek U, Saltz JH, Gurcan MN (2009) Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern recognition 42(6):1093–1103CrossRefPubMedPubMedCentral

31.

Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO (2013) Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Pattern Anal Mach Intell 35(8):1930–1943CrossRefPubMed

32.

Shin M, Jang D, Nam H, Lee K.H., Lee D (2016) Predicting the absorption potential of chemical compounds through a deep learning approach. IEEE/ACM Trans Comput Biol Bioinf 99:1. doi:10.1109/TCBB.2016.2535233

33.

Shlens J (2014) A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100

34.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1–9

35.

Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York

36.

Vincent P, Larochelle H, Bengio Y, Manzagol P.A Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning. ACM, pp 1096–1103

37.

Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

38.

Xu J, Xiang L, Hang R, Wu J (2014) Stacked sparse autoencoder (ssae) based framework for nuclei patch classification on breast cancer histopathology. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, pp 999–1002

39.

Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imag 35(1):119–130CrossRef

40.

Zhang Z, Lyons M, Schuster M, Akamatsu S (1998) Comparison between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron. In: 1998 proceedings third IEEE international conference on automatic face and gesture recognition. IEEE, pp 454–459

Title: Breast cancer cell nuclei classification in histopathology images using deep neural networks
Authors: Yangqin Feng
Lei Zhang
Zhang Yi
Publication date: 01-02-2018
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 2/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1663-9

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Breast cancer cell nuclei classification in histopathology images using deep neural networks

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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