Variable Augmented Network for Invertible Modality Synthesis-Fusion
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Abstract (arXiv)
As an effective way to integrate the information contained in multiple medical images under different modalities, medical image synthesis and fusion have emerged in various clinical applications such as disease diagnosis and treatment planning. In this paper, an invertible and variable augmented network (iVAN) is proposed for medical image synthesis and fusion. In iVAN, the channel number of the network input and output is the same through variable augmentation technology, and data relevance is enhanced, which is conducive to the generation of characterization information. Meanwhile, the invertible network is used to achieve the bidirectional inference processes. Due to the invertible and variable augmentation schemes, iVAN can not only be applied to the mappings of multi-input to one-output and multi-input to multi-output, but also be applied to one-input to multi-output. Experimental results demonstrated that the proposed method can obtain competitive or superior performance in comparison to representative medical image synthesis and fusion methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 2 Sep 2021.
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15 of 46 references.
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3 more
- 2020Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesiscited by 38
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