Image-based Survival Analysis for Lung Cancer Patients using CNNs

D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society. Series B (Methodological)

1972

Earlier work this paper cites.

H. J. W. L. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Cavalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J. Gillies, and P. Lambin, “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature Communications

2014

Earlier work this paper cites.

Springer International Publishing, 2 ed., 2015

F. Harrell, Regression Modelling Strategies · 2015

Earlier work this paper cites.

H. J. W. L. Aerts, E. Rios Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, and P. Lambin, “Data from NSCLC-radiomics,” in The Cancer Imaging Archive

2015

Earlier work this paper cites.

J. Katzman, U. Shaham, J. Bates, A. Cloninger, T. Jiang, and Y. Kluger, “DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network,” 2016

2016

Earlier work this paper cites.

X. Zhu, J. Yao, and J. Huang, “Deep convolutional neural network for survival analysis with pathological images,” in IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2016

Earlier work this paper cites.

Ö. Çiçek, A. Abdulkadir, S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: Learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI)

2016

Cited alongside, same era.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis

2017

Cited alongside, same era.

S. Leger, A. Zwanenburg, K. Pilz, F. Lohaus, A. Linge, K. Zöphel, J. Kotzerke, A. Schreiber, I. Tinhofer, V. Budach, A. Sak, M. Stuschke, P. Balermpas, C. Rödel, U. Ganswindt, C. Belka, S. Pigorsch, S. E. Combs, D. Mönnich, D. Zips, M. Krause, M. Baumann, E. G. C. Troost, S. Löck, and C. Richter, “A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling,” Scientific Reports

2017

Cited alongside, same era.

X. Zhu, J. Yao, F. Zhu, and J. Huang, “Wsisa: Making survival prediction from whole slide histopathological images,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2017

Later among the works it cites.

J. Yao, X. Zhu, F. Zhu, and J. Huang, “Deep correlational learning for survival prediction from multi-modality data,” in Medical Image Computing and Computer Assisted Interventions (MICCAI)

2017

Later among the works it cites.

J. Lao, Y. Chen, Z.-C. Li, Q. Li, J. Zhang, J. Liu, and G. Zhai, “A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme,” Scientific Reports

2017

Later among the works it cites.

J. J. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. Beets-Tan, J.-C. Fillion-Robin, S. Pieper, and H. J. Aerts, “Computational radiomics system to decode the radiographic phenotype,” Cancer Research

2017

Later among the works it cites.