Summary of Cross-modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks, by Xuran Zhu
Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks
by Xuran Zhu
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a deep learning-based approach for medical diagnosis, leveraging domain adaptation techniques to enhance the generalizability of machine learning models across different imaging modalities, such as MRI and CT scans. By combining Convolutional Neural Networks (CNNs) with the Maximum Mean Difference (MMD) method, the study demonstrates improved diagnostic accuracy and efficiency, particularly in resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This approach can help improve medical diagnosis by bridging the gap between different imaging modalities. The paper uses a dataset from Kaggle to train and test its model, showing excellent results that could lead to more reliable diagnostic tools for clinicians. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Machine learning
