ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis

• Published in: IEEE transactions on medical imaging Vol. 41; no. 10; pp. 2598 - 2614
• Main Authors: Dalmaz, Onat, Yurt, Mahmut, Cukur, Tolga
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adversarial; Artificial neural networks; Biomedical imaging; Compression; Computed tomography; Computer applications; Computer architecture; Configuration management; generative; Image contrast; Image synthesis; Learning; Magnetic resonance imaging; Medical image synthesis; Medical imaging; Modules; Neural networks; residual; Subspace constraints; Synthesis; Task analysis; transformer; Transformers; unified; Vision

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of c...