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adilrasheed139 / Disentangled-Representation-Learning-for-Domain-Adaptation-in-Medical-Image-Segmentation

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Investigating DRL DCVAE} for mitigating 15% domain shift in cardiac MRI segmentation. Conclusively proves the Information Bottleneck trade-off: 0.02 generalization gap vs. 0.25 DSC performance. Structural limits of DRL identified for high-detail medical tasks.

python reproducible-research jupyter-notebook pytorch representation-learning variational-autoencoder domain-adaptation information-bottleneck feature-disentanglement adversarial-training domain-generalization conditional-variational-autoencoder generalization-gap medical-imaging-segmentation cardiovascular-imaging disentangled-representation-learning cardiac-mri-segmentation multi-vendor-data deep-learning-in-medicine content-utility-tradeoff
  • Updated Dec 20, 2025
  • Jupyter Notebook

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