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added model card and model weights

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  2. model_weights.h5 +3 -0
README.md ADDED
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+ # CascadeNet-fastmri
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+ ---
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+ tags:
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+ - TensorFlow
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+ - MRI reconstruction
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+ - MRI
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+ datasets:
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+ - fastMRI
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+ ---
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+
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+ This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
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+
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+ ## Model description
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+ For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
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+ This section is WIP.
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+
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+ ## Intended uses and limitations
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+ This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
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+ It cannot be used on multi-coil data.
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+
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+ ## How to use
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+ This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
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+ After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
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+ The framework is TensorFlow.
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+
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+ You can initialize and load the model weights as follows:
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+ ```python
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+ from fastmri_recon.models.functional_models.cascading import cascade_net
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+
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+ model = cascade_net()
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+ model.load_weights('model_weights.h5')
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+ ```
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+
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+ Using the model is then as simple as:
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+ ```python
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+ model([
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+ kspace, # shape: [n_slices, n_rows, n_cols, 1]
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+ mask, # shape: [n_slices, n_rows, n_cols]
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+ ])
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+ ```
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+
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+ ## Limitations and bias
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+ The limitations and bias of this model have not been properly investigated.
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+
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+ ## Training data
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+ This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
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+
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+ ## Training procedure
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+ The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data.
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+ This section is WIP.
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+
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+ ## Evaluation results
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+ This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).
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+
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+ | Contrast | PD | PDFS |
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+ |----------|-------|--------|
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+ | PSNR | 33.98 | 29.88 |
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+ | SSIM | 0.811 | 0.6251 |
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+
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+
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+ ## Bibtex entry
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+ ```
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+ @article{ramzi2020benchmarking,
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+ title={Benchmarking MRI reconstruction neural networks on large public datasets},
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+ author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
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+ journal={Applied Sciences},
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+ volume={10},
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+ number={5},
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+ pages={1816},
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+ year={2020},
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+ publisher={Multidisciplinary Digital Publishing Institute}
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+ }
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+ ```
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+
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