Summary of Cross-modality Guidance-aided Multi-modal Learning with Dual Attention For Mri Brain Tumor Grading, by Dunyuan Xu et al.
Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading
by Dunyuan Xu, Xi Wang, Jinyue Cai, Pheng-Ann Heng
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing need for accurate and efficient diagnosis of brain tumors through Magnetic Resonance Imaging (MRI) protocols. Current manual assessment methods are time-consuming and prone to errors due to the large amount of data and diversity of brain tumor types. To overcome these limitations, the authors propose a novel cross-modality guidance-aided multi-modal learning approach with dual attention for MRI brain tumor grading. The approach utilizes ResNet Mix Convolution as the backbone network for feature extraction, and incorporates dual attention to capture semantic interdependencies in spatial and slice dimensions. A cross-modality guidance-aided module is designed to facilitate information interaction among modalities, leveraging complementary information from different MRI modalities while alleviating noise impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem: how to quickly and correctly diagnose brain tumors using special images called MRIs. Right now, doctors have to look at these images by hand, which takes a long time and can be wrong sometimes. The authors want to make it easier and more accurate with a new way of using information from different MRI images together. They use a special kind of computer network to help the diagnosis, and they also try to get rid of any extra noise that might be in the pictures. |
Keywords
» Artificial intelligence » Attention » Feature extraction » Multi modal » Resnet
