Initial upload of models
Browse files- .gitattributes +2 -0
- Fig1_TA_NA.png +3 -0
- Fig2_BD_CC_FL.png +3 -0
- README.md +21 -0
- best_modelaf2ndab7_221ag12g11.h5 +3 -0
- best_moderRl_RHID2_1mo.h5 +3 -0
- bestac22_mode3l_512m2_m21.h5 +3 -0
- builder1_mode1l1abW512_1_11211z1p1rt_.h5 +3 -0
- direct7_11ag23f11.h5 +3 -0
.gitattributes
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Fig1_TA_NA.png
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Git LFS Details
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Git LFS Details
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README.md
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# pyMEAL: Multi-Encoder-Augmentation-Aware-Learning
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pyMEAL is a multi-encoder framework for augmentation-aware learning that accurately performs CT-to-T1-weighted MRI translation under diverse augmentations. It utilizes four dedicated encoders and three fusion strategies, concatenation (CC), fusion layer (FL), and controller block (BD), to capture augmentation-specific features. MEAL-BD outperforms conventional augmentation methods, achieving SSIM > 0.83 and PSNR > 25 dB in CT-to-T1w translation.
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## Dependecies
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tensorflow
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matplotlib
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SimpleITK
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scipy
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antspyx
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## Tutorials
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To use the different modules of pyMEAL, please refer to the tutorial section in our GitHub repository (https://github.com/ai-vbrain/pyMEAL)
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## How to get support?
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Just write to [email protected] or [email protected]
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best_modelaf2ndab7_221ag12g11.h5
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