2019-05-24-xia19a.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization	Pseudo healthy synthesis, i.e. the creation of a subject-specific ‘healthy’ image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.	inproceedings	Proceedings of Machine Learning Research	xia19a	0	Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization	512	526	512-526	512	false	Xia, Tian and Chartsias, Agisilaos and Tsaftaris, Sotirios A.	given family Tian Xia given family Agisilaos Chartsias given family Sotirios A. Tsaftaris	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/xia19a/xia19a.pdf