Cross-Model Transformer Method for Medical Image Synthesis

Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (G...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zebin Hu, Hao Liu, Zhendong Li, Zekuan Yu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6acf5f7704c9454cba88120c0adc4688
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6acf5f7704c9454cba88120c0adc4688
record_format	dspace
spelling	oai:doaj.org-article:6acf5f7704c9454cba88120c0adc46882021-11-15T01:19:19ZCross-Model Transformer Method for Medical Image Synthesis1099-052610.1155/2021/5624909https://doaj.org/article/6acf5f7704c9454cba88120c0adc46882021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5624909https://doaj.org/toc/1099-0526Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (GAN) models are applied to many medical image synthesis tasks and show prior performance, since they enable to capture structural details clearly. However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. Therefore, the long-range dependencies have been destroyed in this processing. To address this issue, we introduce a double-scale deep learning method for cross-modal medical image synthesis. More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long-range dependencies to learning global feature through global discriminator based on transformer architecture. To evaluate the effectiveness of double-scale GAN, we conduct folds of experiments on the standard benchmark IXI dataset and experimental results demonstrate the effectiveness of our method.Zebin HuHao LiuZhendong LiZekuan YuHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Zebin Hu Hao Liu Zhendong Li Zekuan Yu Cross-Model Transformer Method for Medical Image Synthesis
description	Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (GAN) models are applied to many medical image synthesis tasks and show prior performance, since they enable to capture structural details clearly. However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. Therefore, the long-range dependencies have been destroyed in this processing. To address this issue, we introduce a double-scale deep learning method for cross-modal medical image synthesis. More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long-range dependencies to learning global feature through global discriminator based on transformer architecture. To evaluate the effectiveness of double-scale GAN, we conduct folds of experiments on the standard benchmark IXI dataset and experimental results demonstrate the effectiveness of our method.
format	article
author	Zebin Hu Hao Liu Zhendong Li Zekuan Yu
author_facet	Zebin Hu Hao Liu Zhendong Li Zekuan Yu
author_sort	Zebin Hu
title	Cross-Model Transformer Method for Medical Image Synthesis
title_short	Cross-Model Transformer Method for Medical Image Synthesis
title_full	Cross-Model Transformer Method for Medical Image Synthesis
title_fullStr	Cross-Model Transformer Method for Medical Image Synthesis
title_full_unstemmed	Cross-Model Transformer Method for Medical Image Synthesis
title_sort	cross-model transformer method for medical image synthesis
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/6acf5f7704c9454cba88120c0adc4688
work_keys_str_mv	AT zebinhu crossmodeltransformermethodformedicalimagesynthesis AT haoliu crossmodeltransformermethodformedicalimagesynthesis AT zhendongli crossmodeltransformermethodformedicalimagesynthesis AT zekuanyu crossmodeltransformermethodformedicalimagesynthesis
_version_	1718428989876338688

Cross-Model Transformer Method for Medical Image Synthesis

Ejemplares similares