FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios-Reference-Cited by-同舟云学术

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios

Published:2024-01-19 Issue: Volume: Page:74-79
ISSN:
Container-title:Proceedings of the 2024 7th International Conference on Image and Graphics Processing
language:
Short-container-title:

Author:

Yang Rui¹^ORCID,Wang Xiao²^ORCID,Xu Xin¹^ORCID

Affiliation:

1. Wuhan University of Science and Technology, China and Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, China

2. Wuhan University of Science and Technology, China and Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3647649.3647661

Reference40 articles.

1. Reza Azad, Nika Khosravi, and Dorit Merhof. Smu-net: Style matching u-net for brain tumor segmentation with missing modalities. arXiv preprint arXiv:2204.02961, 2022.

2. Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, and Pheng-Ann Heng. Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 447–456. Springer, 2019.

3. Learning with Privileged Multimodal Knowledge for Unimodal Segmentation

4. Explaining Knowledge Distillation by Quantifying the Knowledge

5. Measures of the Amount of Ecologic Association Between Species

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