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Initial upload of models

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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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+
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+ ## Dependecies
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+
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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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+
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+ ## How to get support?
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