Comparison of deep transfer learning strategies for digital pathology

Mormont, Romain; Geurts, Pierre; Marée, Raphaël

Download

Paper published in a book (Scientific congresses and symposiums)

Comparison of deep transfer learning strategies for digital pathology

Mormont, Romain; Geurts, Pierre; Marée, Raphaël

2018 • In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Peer reviewed

Permalink
https://hdl.handle.net/2268/222511

Files (5)Send to Details Statistics Bibliography Similar publications

Files

Full Text

mormont2018-comparison.pdf

Publisher postprint (2.19 MB)

Download

Annexes

mormont2018comparison-supp.pdf

Publisher postprint (2.03 MB)

Supplementary materials

Download

mormont2018comparison-poster.pdf

Publisher postprint (6.27 MB)

Poster CVMI 2018

Download

code.zip

Publisher postprint (110.16 kB)

Code

Download

mormont2018comparison-slides.pdf

Publisher postprint (10.13 MB)

Slides CVMI 2018

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

deep learning; transfer learning; digital pathology; object recognition

Abstract :

[en] In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

Research Center/Unit :

Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège

Disciplines :

Computer science

Author, co-author :

Mormont, Romain ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique

Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique

Marée, Raphaël ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Language :

English

Title :

Comparison of deep transfer learning strategies for digital pathology

Alternative titles :

[fr] Comparaison de stratégies de transfert profond pour la pathologie digitale

Publication date :

2018

Event name :

3rd Computer Vision for Microscopy Image Analysis (CVMI) workshop at the 2018 IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR)

Event organizer :

IEEE Computer Society, Computer Vision Foundation

Event place :

Salt Lake City, United States

Event date :

du 18 juin 2018 au 22 juin 2018

Audience :

International

Main work title :

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Publisher :

IEEE

Peer reviewed :

Peer reviewed

Available on ORBi :

since 21 April 2018

Statistics

Number of views

832 (130 by ULiège)

Number of downloads

1207 (87 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

Bibliography

J. Antony, K. McGuinness, N. E. O'Connor, and K. Moran. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In Pattern Recognition (ICPR), 2016 23rd International Conference on, pages 1195-1200. IEEE, 2016.
Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan. Chest pathology detection using deep learning with non-medical training. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pages 294-297. IEEE, 2015.
N. Bayramoglu and J. Heikkilä. Transfer learning for cell nuclei classification in histopathology images. In European Conference on Computer Vision, pages 532-539. Springer, 2016.
L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.
F. Chollet et al. Keras. https://github. com/keras-team/keras, 2015.
F. Ciompi, B. de Hoop, S. J. van Riel, K. Chung, E. T. Scholten, M. Oudkerk, P. A. de Jong, M. Prokop, and B. van Ginneken. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2d views and a convolutional neural network outof-the-box. Medical image analysis, 26(1):195-202, 2015.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248-255. IEEE, 2009.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning, pages 647-655, 2014.
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639):115, 2017.
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. Journal of machine learning research, 9(Aug):1871-1874, 2008.
P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine learning, 63(1):3-42, 2006.
H. Greenspan, B. van Ginneken, and R. M. Summers. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5):1153-1159, 2016.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402-2410, 2016.
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389-422, 2002.
Z. Han, B. Wei, Y. Zheng, Y. Yin, K. Li, and S. Li. Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific reports, 7(1):4172, 2017.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704. 04861, 2017.
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, volume 1, page 3, 2017.
B. Kieffer, M. Babaie, S. Kalra, and H. Tizhoosh. Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks. arXiv preprint arXiv:1710. 05726, 2017.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412. 6980, 2014.
O. Z. Kraus, B. T. Grys, J. Ba, Y. Chong, B. J. Frey, C. Boone, and B. J. Andrews. Automated analysis of highcontent microscopy data with deep learning. Molecular systems biology, 13(4):924, 2017.
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
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, 42:60-88, 2017.
R. Marée. The need for careful data collection for pattern recognition in digital pathology. Journal of pathology informatics, 8, 2017.
R. Marée, S. Dallongeville, J.-C. Olivo-Marin, and V. Meas-Yedid. An approach for detection of glomeruli in multisite digital pathology. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 1033-1036. IEEE, 2016.
R. Marée, P. Geurts, and L. Wehenkel. Towards generic image classification using tree-based learning: An extensive empirical study. Pattern Recognition Letters, 74:17-23, 2016.
R. Marée, L. Rollus, B. Stévens, R. Hoyoux, G. Louppe, R. Vandaele, J.-M. Begon, P. Kainz, P. Geurts, and L. Wehenkel. Collaborative analysis of multi-gigapixel imaging data using cytomine. Bioinformatics, 32(9):1395-1401, 2016.
M. McCann, C. Castro, J. Ozolek, B. Parvin, and J. Kovacevic. Automated histology analysis: opportunities for signal processing. IEEE Signal Processing, 2014.
S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345-1359, 2010.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
H. Ravishankar, P. Sudhakar, R. Venkataramani, S. Thiruvenkadam, P. Annangi, N. Babu, and V. Vaidya. Understanding the mechanisms of deep transfer learning for medical images. In Deep Learning and Data Labeling for Medical Applications, pages 188-196. Springer, 2016.
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: An astounding baseline for recognition. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 512-519. IEEE, 2014.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312. 6229, 2013.
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5):1285-1298, 2016.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. 1556, 2014.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, volume 4, page 12, 2017.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818-2826, 2016.
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299-1312, 2016.
B. van Ginneken, A. A. Setio, C. Jacobs, and F. Ciompi. Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pages 286-289. IEEE, 2015.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320-3328, 2014.
M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818-833. Springer, 2014.