We lifted 6 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| arnab39/FewShot_GAN-Unet3D | canonical | 1 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| gaussian_nll | Ran | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/operations.py code served (permissive licence) · get_code("96330c807f450c14") |
| compute_weighted_fm_loss | Not yet run | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/utils.py code served (permissive licence) · get_code("7a4e9b0cbf3d8e35") |
| conv3d | Not yet run | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/operations.py code served (permissive licence) · get_code("6024f115cb83d9ca") |
| deconv3d | Not yet run | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/operations.py code served (permissive licence) · get_code("eb6e2b8cd5e1d13b") |
| load_model | Not yet run | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/utils.py code served (permissive licence) · get_code("3e4080fa699e175f") |
| recompose3D_overlap | Not yet run | arnab39/FewShot_GAN-Unet3D/tensorflow/lib/utils.py code served (permissive licence) · get_code("f4da2c52cbf42320") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We address the problem of segmenting 3D multi-modal medical images in scenarios where very few labeled examples are available for training. Leveraging the recent success of adversarial learning for semi-supervised segmentation, we propose a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images. The proposed method prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network. Our work extends current adversarial learning approaches, which focus on 2D single-modality images, to the more challenging context of 3D volumes of multiple modalities. The proposed method is evaluated on the problem of segmenting brain MRI from the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement is reported, compared to state-of-art segmentation networks trained in a fully-supervised manner. In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches. Our code is publicly available at https://github.com/arnab39/FewShot_GAN-Unet3D
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1810.12241")
get_code_for_paper("1810.12241")
have("1810.12241")
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