ArticleIEEE transactions on medical imaging2021
Multi-Domain Image Completion for Random Missing Input Data.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Laplacian filter attention with style transfer GAN for brain tumor MRI imputation.Scientific reports · 2025Article
- Conditional generative learning for medical image imputation.Scientific reports · 2024Article
- One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.IEEE transactions on medical imaging · 2023Article
- Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective.Medical image analysis · 2023Article
- PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers.IEEE transactions on medical imaging · 2022Article
- Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.Medical physics · 2022Article
- Novel-view X-ray projection synthesis through geometry-integrated deep learning.Medical image analysis · 2022Article
- A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain.PloS one · 2022Article
- Representation Disentanglement for Multi-modal Brain MRI Analysis.Information processing in medical imaging : proceedings of the ... conference · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.