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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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license: mit
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tags:
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- keras
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- medical-imaging
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- deep-learning
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- .h5-model
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framework: keras
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task: image-translation
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---
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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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