Summary of Efficient Slice Anomaly Detection Network For 3d Brain Mri Volume, by Zeduo Zhang et al.
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume
by Zeduo Zhang, Yalda Mohsenzadeh
First submitted to arxiv on: 28 Aug 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 proposed Simple Slice-based Network (SimpleSliceNet) is a novel anomaly detection framework that leverages a pre-trained model on ImageNet and fine-tunes it on MRI data to extract features from 2D slices. These features are then aggregated to perform anomaly detection tasks on 3D brain MRI volumes. The model integrates a conditional normalizing flow to calculate log likelihood of features and employs the Semi-Push-Pull Mechanism to enhance anomaly detection accuracy. Compared to state-of-the-art reconstruction-based models, SimpleSliceNet exhibits improved performance, reduced memory usage, and faster computation times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Simple Slice-based Network is a new way to find unusual things in brain MRI pictures. Current methods are good for industrial data but struggle with medical data because “normal” and “abnormal” can mean different things. The proposed method uses a pre-trained model and fine-tunes it on MRI data to look at 2D slices, which makes the computation faster and more accurate. This method is better than existing ones for large-scale brain volumes. |
Keywords
» Artificial intelligence » Anomaly detection » Log likelihood
