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README.md
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---
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language: en
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tags:
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- diffusion-models
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- medical-imaging
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- glioma
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- synthetic-data
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- MRI
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license: mit
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datasets:
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- BraTS2024
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model-index:
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- name: GliomaGen
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results:
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- task:
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type: image-generation
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dataset:
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name: BraTS2024 Adult Post-Treatment Glioma
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type: medical-imaging
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metrics:
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- name: FID (t1c)
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type: frechet-inception-distance
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value: 55.2028 ± 3.7446
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- name: FID (t2w)
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type: frechet-inception-distance
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value: 54.9974 ± 3.2271
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- name: KID (t1c)
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type: kernel-inception-distance
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value: 0.0293 ± 0.0019
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- name: MS-SSIM (t1c)
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type: multi-scale-structural-similarity
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value: 0.7647 ± 0.2106
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---
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# GliomaGen: Conditional Diffusion for Post-Treatment Glioma MRI Generation
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GliomaGen is a generative diffusion model tailored for synthesizing post-treatment glioma MRI images based on anatomical masks. It leverages a modified **Med-DDPM** architecture to create high-fidelity MRI images conditioned on segmented anatomical features.
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## Model Overview
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GliomaGen aims to address data scarcity in post-treatment glioma segmentation tasks by expanding existing datasets with synthetic, high-quality MRI volumes. The model takes anatomical masks as input and generates multi-modal MRI scans conditioned on segmentation labels.
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## Model Performance
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### **Quantitative Metrics**
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| Modality | FID (↓) | KID (↓) | MS-SSIM (↑) |
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|----------|--------|--------|-------------|
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| t1c | 55.20 ± 3.74 | 0.0293 ± 0.0019 | 0.7647 ± 0.2106 |
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| t2w | 54.99 ± 3.23 | 0.0291 ± 0.0010 | 0.6513 ± 0.2881 |
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| t1n | 58.46 ± 3.86 | 0.0305 ± 0.0011 | 0.7005 ± 0.2585 |
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| t2f | 70.42 ± 4.17 | 0.0370 ± 0.0018 | 0.7842 ± 0.1551 |
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## Usage
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### **Loading the Model**
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To use GliomaGen for MRI generation, see the [GitHub repository](https://github.com/elijahrenner/gliomagen).
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