Summary of Discrepancy-based Diffusion Models For Lesion Detection in Brain Mri, by Keqiang Fan et al.
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI
by Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
First submitted to arxiv on: 8 May 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 proposes a novel framework for lesion detection in brain MRI, called discrepancy distribution medical diffusion (DDMD). The method incorporates distinctive discrepancy features, deviating from conventional approaches that rely on image-level annotations or original brain modalities. The DDMD model translates inconsistency in image-level annotations into distribution discrepancies among heterogeneous samples, retaining pixel-wise uncertainty and facilitating an implicit ensemble of segmentation. This approach enhances overall detection performance. Experiments conducted on the BRATS2020 benchmark dataset demonstrate great performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lesion detection in brain MRI is important for medical imaging applications. Researchers have used diffusion probabilistic models (DPMs) to generate images, but these models rely heavily on labelled datasets, making it difficult to apply them to medical images. Some DPM-based methods for lesion detection use image-level annotations, but this paper proposes a new approach that doesn’t require these labels. Instead, the model uses “discrepancy features” to detect lesions. This method is tested on a brain tumour detection dataset and performs well compared to other approaches. |
Keywords
» Artificial intelligence » Diffusion
