nach oben

Erschienen in:

2017 | Supplement | Buchkapitel

Deep Correlational Learning for Survival Prediction from Multi-modality Data

verfasst von : Jiawen Yao, Xinliang Zhu, Feiyun Zhu, Junzhou Huang

Erschienen in: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Technological advances have created a great opportunity to provide multi-view data for patients. However, due to the large discrepancy between different heterogeneous views, traditional survival models are unable to efficiently handle multiple modalities data as well as learn very complex interactions that can affect survival outcomes in various ways. In this paper, we develop a Deep Correlational Survival Model (DeepCorrSurv) for the integration of multi-view data. The proposed network consists of two sub-networks, view-specific and common sub-network. To remove the view discrepancy, the proposed DeepCorrSurv first explicitly maximizes the correlation among the views. Then it transfers feature hierarchies from view commonality and specifically fine-tunes on the survival regression task. Extensive experiments on real lung and brain tumor data sets demonstrated the effectiveness of the proposed DeepCorrSurv model using multiple modalities data across different tumor types.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Groupwise Registration of MR Brain Images Containing Tumors via Spatially Constrained Low-Rank Based Image Recovery

Nächstes Kapitel Compressed Sensing on Multi-pinhole Collimator SPECT Camera for Sentinel Lymph Node Biopsy

Andrew, G., Arora, R., Bilmes, J.A., Livescu, K.: Deep canonical correlation analysis. In: ICML, pp. 1247–1255 (2013)

Bair, E., Hastie, T., Paul, D., Tibshirani, R.: Prediction by supervised principal components. J. Am. Stat. Assoc. 101(473), 119–137 (2006)MathSciNetCrossRef

Bair, E., Tibshirani, R.: Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2(4), E108 (2004)CrossRef

Bøvelstad, H.M., Nygård, S., Størvold, H.L., Aldrin, M., Borgan, Ø., Frigessi, A., Lingjærde, O.C.: Predicting survival from microarray dataa comparative study. Bioinformatics 23(16), 2080–2087 (2007)CrossRef

Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., et al.: Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), R100 (2006)CrossRef

Cox, D.R.: Regression models and life-tables. J. Roy. Stat. Soc.: Ser. B (Methodol.) 34, 187–220 (1972)MathSciNetMATH

Du, L., Huang, H., Yan, J., Kim, S., Risacher, S.L., Inlow, M., Moore, J.H., Saykin, A.J., Shen, L., Initiative, A.D.N., et al.: Structured sparse canonical correlation analysis for brain imaging genetics: an improved graphnet method. Bioinformatics 32(10), 1544 (2016)CrossRef

Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)CrossRef

Kalbfleisch, J.D., Prentice, R.L.: The Statistical Analysis of Failure Time Data, vol. 360. Wiley, Hoboken (2011)MATH

10.

Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., et al.: Mutational landscape and significance across 12 major cancer types. Nature 502(7471), 333–339 (2013)CrossRef

11.

Katzman, J., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: Deep survival: a deep cox proportional hazards network. arXiv preprint arXiv:1606.00931 (2016)

12.

Li, Y., Wang, J., Ye, J., Reddy, C.K.: A multi-task learning formulation for survival analysis. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1715–1724 (2016)

13.

Mayr, A., Schmid, M.: Boosting the concordance index for survival data-a unified framework to derive and evaluate biomarker combinations. PLoS one 9(1), e84483 (2014)CrossRef

14.

Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688, May 2016. http://arxiv.org/abs/1605.02688

15.

Tibshirani, R., et al.: The lasso method for variable selection in the Cox model. Stat. Med. 16(4), 385–395 (1997)CrossRef

16.

Yao, J., Ganti, D., Luo, X., Xiao, G., Xie, Y., Yan, S., Huang, J.: Computer-assisted diagnosis of lung cancer using quantitative topology features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 288–295. Springer, Cham (2015). doi:10.1007/978-3-319-24888-2_35CrossRef

17.

Yao, J., Wang, S., Zhu, X., Huang, J.: Imaging biomarker discovery for lung cancer survival prediction. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 649–657. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_75CrossRef

18.

Yuan, Y., Failmezger, H., Rueda, O.M., Ali, H.R., Gräf, S., Chin, S.F., Schwarz, R.F., Curtis, C., Dunning, M.J., Bardwell, H., et al.: Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4(157), 157ra143 (2012)CrossRef

19.

Yuan, Y., Van Allen, E.M., Omberg, L., Wagle, N., Amin-Mansour, A., Sokolov, A., Byers, L.A., Xu, Y., Hess, K.R., Diao, L., et al.: Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol. 32(7), 644–652 (2014)CrossRef

20.

Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 544–547. IEEE (2016)

21.

Zhu, X., Yao, J., Luo, X., Xiao, G., Xie, Y., Gazdar, A., Huang, J.: Lung cancer survival prediction from pathological images and genetic data - an integration study. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1173–1176 (2016)

22.

Zhu, X., Yao, J., Xiao, G., Xie, Y., Rodriguez-Canales, J., Parra, E.R., Behrens, C., Wistuba, I.I., Huang, J.: Imaging-genetic data mapping for clinical outcome prediction via supervised conditional gaussian graphical model. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 455–459. IEEE (2016)

Titel: Deep Correlational Learning for Survival Prediction from Multi-modality Data
verfasst von: Jiawen Yao
Xinliang Zhu
Feiyun Zhu
Junzhou Huang
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
Print ISBN: 978-3-319-66184-1

Electronic ISBN: 978-3-319-66185-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-66185-8_46

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"