Mutual Information-based Disentangled Neural Networks for Classifying
Unseen Categories in Different Domains: Application to Fetal Ultrasound
Imaging
- URL: http://arxiv.org/abs/2011.00739v2
- Date: Tue, 6 Apr 2021 17:11:52 GMT
- Title: Mutual Information-based Disentangled Neural Networks for Classifying
Unseen Categories in Different Domains: Application to Fetal Ultrasound
Imaging
- Authors: Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer, Alberto Gomez,
David F.A. Lloyd, Daniel Rueckert, Bernhard Kainz
- Abstract summary: Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features.
We propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.
We extensively evaluate the proposed method on fetal ultrasound datasets for two different image classification tasks.
- Score: 10.504733425082335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks exhibit limited generalizability across images with
different entangled domain features and categorical features. Learning
generalizable features that can form universal categorical decision boundaries
across domains is an interesting and difficult challenge. This problem occurs
frequently in medical imaging applications when attempts are made to deploy and
improve deep learning models across different image acquisition devices, across
acquisition parameters or if some classes are unavailable in new training
databases. To address this problem, we propose Mutual Information-based
Disentangled Neural Networks (MIDNet), which extract generalizable categorical
features to transfer knowledge to unseen categories in a target domain. The
proposed MIDNet adopts a semi-supervised learning paradigm to alleviate the
dependency on labeled data. This is important for real-world applications where
data annotation is time-consuming, costly and requires training and expertise.
We extensively evaluate the proposed method on fetal ultrasound datasets for
two different image classification tasks where domain features are respectively
defined by shadow artifacts and image acquisition devices. Experimental results
show that the proposed method outperforms the state-of-the-art on the
classification of unseen categories in a target domain with sparsely labeled
training data.
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