Comparison of Deep Transfer Learning Strategies for Digital Pathology
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.
Cite
Text
Mormont et al. "Comparison of Deep Transfer Learning Strategies for Digital Pathology." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00303Markdown
[Mormont et al. "Comparison of Deep Transfer Learning Strategies for Digital Pathology." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/mormont2018cvprw-comparison/) doi:10.1109/CVPRW.2018.00303BibTeX
@inproceedings{mormont2018cvprw-comparison,
title = {{Comparison of Deep Transfer Learning Strategies for Digital Pathology}},
author = {Mormont, Romain and Geurts, Pierre and Marée, Raphaël},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {2262-2271},
doi = {10.1109/CVPRW.2018.00303},
url = {https://mlanthology.org/cvprw/2018/mormont2018cvprw-comparison/}
}