/representation-shift

This is the official code of the Representation Shift metric from "Measuring Domain Shift for Deep Learning in Histopathology" by Stacke et al.

Primary LanguageJupyter Notebook

Representation Shift

This is the official repo for the paper "Measuring Domain Shift for Deep Learning in Histopathology".

Requirements

For calculating the representation shift using Wasserstein distance, the implementation from scipy was used. To run example code, the following python libs are needed: numpy, scipy, torch, torchvision.

Example code

In example_repr_shift_calc.ipynb we show an example usage, where a pretrained Resnet18 model is used, and the representation shift is calculated for both in-distribution and out-of-distribution data. This example can easily be extended to other model architectures, and other datasets.

Cite

@article{stacke_2021,
	title = {Measuring {Domain} {Shift} for {Deep} {Learning} in {Histopathology}},
	volume = {25},
	issn = {2168-2208},
	doi = {10.1109/JBHI.2020.3032060},
	number = {2},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	author = {Stacke, Karin and Eilertsen, Gabriel and Unger, Jonas and Lundström, Claes},
	month = feb,
	year = {2021},
}