Inter-Semantic Domain Adversarial in Histopathological Images
- URL: http://arxiv.org/abs/2201.09041v1
- Date: Sat, 22 Jan 2022 12:55:59 GMT
- Title: Inter-Semantic Domain Adversarial in Histopathological Images
- Authors: Nicolas Dumas, Valentin Derang\`ere, Laurent Arnould, Sylvain Ladoire,
Louis-Oscar Morel, Nathan Vin\c{c}on
- Abstract summary: In computer vision, data shift has proven to be a major barrier for safe and robust deep learning applications.
It is important to understand to what extent a model can be made robust against data shift using all available data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In computer vision, data shift has proven to be a major barrier for safe and
robust deep learning applications. In medical applications, histopathological
images are often associated with data shift and they are hardly available. It
is important to understand to what extent a model can be made robust against
data shift using all available data. Here, we first show that domain
adversarial methods can be very deleterious if they are wrongly used. We then
use domain adversarial methods to transfer data shift invariance from one
dataset to another dataset with different semantics and show that domain
adversarial methods are efficient inter-semantically with similar performance
than intra-semantical domain adversarial methods.
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