├── LICENSE
├── README.md
└── cohension_TMI.png


/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Yanda Meng
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # Dual_Adaptive_Graph_Reasoning
 2 | 
 3 | - please refer to the implementation code: https://doi.org/10.5281/zenodo.6539438
 4 | 
 5 | - Prepare your data.
 6 | 
 7 | - Download the pre-train Res2Net parameters and put it into ./lib/res_weight/
 8 | - https://drive.google.com/file/d/16_bh91WdaAKPFnwkMkDNMaQUTTIHboWi/view?usp=sharing
 9 | 
10 | # Train
11 | - run python train.py
12 | 
13 | 
14 | # Test
15 | - Download our pre-trained model's parameter then put it into your-own-path
16 | - https://drive.google.com/file/d/12uqe20GdX3-DxeszPqdlIB2o8svyh_vs/view?usp=sharing
17 | - run test.py
18 | 
19 | # Qualitative Results of Coherence Comparison
20 | ![alt text](https://github.com/smallmax00/Dual_Adaptive_Graph_Reasoning/blob/main/cohension_TMI.png)
21 | 
22 | 
23 | 
24 | # Citation
25 | If you find our work useful or our work gives you any insights, please cite:
26 | ```
27 | @article{meng2022dual,
28 |   title={Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation with Dual Adaptive Graph Convolutional Networks},
29 |   author={Meng, Yanda and Zhang, Hongrun and Zhao, Yitian and Gao, Dongxu and Hamill, Barbra and Patri, Godhuli and Peto, Tunde and Madhusudhan, Savita and Zheng, Yalin},
30 |   journal={IEEE Transactions on Medical Imaging},
31 |   year={2022},
32 |   publisher={IEEE}
33 | }
34 | 
35 | ```
36 | 


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/cohension_TMI.png:
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https://raw.githubusercontent.com/smallmax00/Dual_Adaptive_Graph_Reasoning/050da63dbd6f0040b993e4d7d7c379ba523d6175/cohension_TMI.png


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