About: Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging
Creator	Akar, Gozde Baydar, Bora Ozkan, Savas
source	ArXiv
abstract	Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detailed analysis. For instance, CT and MRI have advantages over each other in terms of imaging quality, artifacts, and output characteristics that lead to differential diagnosis. The capacity of current segmentation techniques is only allow to work for an individual domain due to their differences. However, the models that are capable of working on all modalities are essentially needed for a complete solution. Furthermore, robustness is drastically affected by the number of samples in the training step, especially for deep learning models. Hence, there is a necessity that all available data regardless of data domain should be used for reliable methods. For this purpose, this manuscript aims to implement a novel model that can learn robust representations from cross-domain data by encapsulating distinct and shared patterns from different modalities. Precisely, covariate shift property is retained with structural modification and adversarial loss where sparse and rich representations are obtained. Hence, a single parameter set is used to perform cross-domain segmentation task. The superiority of the proposed method is that no information related to modalities are provided in either training or inference phase. The tests on CT and MRI liver data acquired in routine clinical workflows show that the proposed model outperforms all other baseline with a large margin. Experiments are also conducted on Covid-19 dataset that it consists of CT data where significant intra-class visual differences are observed. Similarly, the proposed method achieves the best performance.
has issue date	2020-06-08(xsd:dateTime)
has license	arxiv
sha1sum (hex)	9e2d42429fb7655af8dfe5be4a73791935f857ec
resource representing a document's title	Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging
resource representing a document's body	covid:9e2d42429fb7655af8dfe5be4a73791935f857ec#body_text
is schema:about of	named entity 'reliable' named entity 'instance' named entity 'The' named entity 'images' named entity 'current' »more»

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