DRM-VAE: A Dual Residual Multi Variational Auto-Encoder for Brain Tumor Segmentation with Missing Modalities-Reference-Cited by-同舟云学术

DRM-VAE: A Dual Residual Multi Variational Auto-Encoder for Brain Tumor Segmentation with Missing Modalities

Published:2021-12-17 Issue: Volume: Page:
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Container-title:2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE)
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Author:

Zhu Yian,Wang Shaoyu,Hu Yun,Ma Xiao,Qin Yanxia,Xie Jianyun

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9674218/9674212/09674673.pdf?arnumber=9674673

Reference14 articles.

1. Auto-Encoding Variational Bayes;kingma;ICLRE,2014

2. Multimodal generative models for scalable weakly-supervised learning;wu;Proceedings of the 32Nd International Conference on Neural Information Processing Systems,2018

3. Learning hierarchical features from deep generative models;zhao;International Conference on Machine Learning,2017

4. NVAE: A deep hierarchical variational autoencoder;vahdat;Neural Information Processing Systems,2020

5. Brain Tumor Segmentation for Missing Modalities by Supplementing Missing Features

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