IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
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Abstract (arXiv)
Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of lesions. Such multi-parameter MRI information has been shown to improve radiologist performance in lesion classification, as well as improving the performance of artificial intelligence models in various tasks. However, obtaining multi-parameter MRI makes the examination costly in both financial and time perspectives, and there may be safety concerns for special populations, thus making acquisition of the full spectrum of MRI sequences less durable. In this study, different than naive input fusion or feature concatenation from existing MRI parameters, a novel Integrated MRI Multi-Parameter reinfOrcement fusion generatoR wiTh AtteNTion Network (IMPORTANT-Net) is developed to generate missing parameters. First, the parameter reconstruction module is used to encode and restore the existing MRI parameters to obtain the corresponding latent representation information at any scale level. Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information. Finally, a reinforcement fusion scheme embedded in a V--shape generation module is used to combine the hierarchical representations to generate the missing MRI parameter. Results showed that our IMPORTANT-Net is capable of generating missing MRI parameters and outperforms comparable state-of-the-art networks. Our code is available at https://github.com/Netherlands-Cancer-Institute/MRIIMPORTANTNET.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 3 Feb 2023.
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17 of 43 references.
References · 17
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2022Generative Adversarial Networkscited by 5,603
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
- 2017Generative Adversarial Networks: An Overviewcited by 588
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
- 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
- 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
- 2018MedGAN: Medical Image Translation using GANscited by 152
- 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
- 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
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- 2020Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesiscited by 86
- 2019mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesiscited by 34
- 2018Deep CT to MR Synthesis using Paired and Unpaired Datacited by 31
- 2018Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNITcited by 18
- 2018SUSAN: Segment Unannotated image Structure using Adversarial Networkcited by 9
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Citing papers per year: 2023: 1.
1 paper.