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README.md
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license: mit
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---
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license: mit
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---
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# RetinaGAN
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Code Repository for: [**High-Fidelity Diabetic Retina Fundus Image Synthesis from Freestyle Lesion Maps**](https://opg.optica.org/abstract.cfm?uri=boe-14-2-533)
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## About
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RetinaGAN a two-step process for generating photo-realistic retinal Fundus images based on artificially generated or free-hand drawn semantic lesion maps.
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StyleGAN is modified to be conditional in to synthesize pathological lesion maps based on a specified DR grade (i.e., grades 0 to 4). The DR Grades are defined by the International Clinical Diabetic Retinopathy (ICDR) disease severity scale; no apparent retinopathy, {mild, moderate, severe} Non-Proliferative Diabetic Retinopathy (NPDR), and Proliferative Diabetic Retinopathy (PDR). The output of the network is a binary image with seven channels instead of class colors to avoid ambiguity.
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The generated label maps are then passed through SPADE, an image-to-image translation network, to turn them into photo-realistic retina fundus images. The input to the network are one-hot encoded labels.
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## Usage
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Download model checkpoints (see [here](checkpoints/README.md) for details) and run the model via Streamlit. Start the app via `streamlit run web_demo.py`.
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## Example Images
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Example retina Fundus images synthesised from Conditional StyleGAN generated lesion maps. Top row: synthetically generated lesion maps based on DR grade by Conditional StyleGAN. Other rows: synthetic Fundus images generated by SPADE. Images are generated sequentially with random seed and are **not** cherry picked.
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| grade 0 | grade 1 | grade 2 | grade 3 | grade 4 |
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|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|
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## Cite this work
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If you find this work useful for your research, give us a kudos by citing:
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```
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@article{hou2023high,
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title={High-fidelity diabetic retina fundus image synthesis from freestyle lesion maps},
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author={Hou, Benjamin},
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journal={Biomedical Optics Express},
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volume={14},
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number={2},
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pages={533--549},
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year={2023},
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publisher={Optica Publishing Group}
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}
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```
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