Comparison of Deep Transfer Learning Strategies for Digital Pathology

Romain Mormont, Pierre Geurts, Raphael Maree; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2262-2271

Abstract


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.

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[bibtex]
@InProceedings{Mormont_2018_CVPR_Workshops,
author = {Mormont, Romain and Geurts, Pierre and Maree, Raphael},
title = {Comparison of Deep Transfer Learning Strategies for Digital Pathology},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}