Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
| Autor: | Qingjie Meng, Nick Pawlowski, Bernhard Kainz, Daniel Rueckert |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Multi-task learning Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Regularization (mathematics) Image properties Synthetic data Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Artificial Intelligence & Image Processing business.industry Deep learning Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis ISBN: 9783030328740 SUSI/PIPPI@MICCAI Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis |
| Popis: | One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are maximally informative with respect to relevant tasks, while an adversarial regularization encourages these features to be disentangled and minimally informative about irrelevant tasks. We aim to use the disentangled representations to generalize the applicability of deep neural networks. We demonstrate the advantages of the proposed method on synthetic data as well as fetal ultrasound images. Our experiments illustrate that our method is capable of learning disentangled internal representations. It outperforms baseline methods in multiple tasks, especially on images with new properties, e.g. previously unseen artifacts in fetal ultrasound. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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