We lifted 17 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| ZJUGiveLab/UNet-Version | canonical | 1 of 1 |
| hamidriasat/UNet-3-Plus | pwc_unofficial | 5 of 13 |
| Owais-Ansari/Unet3plus | reimplementation | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| BCE_loss | Ran | ZJUGiveLab/UNet-Version/loss/bceLoss.py pointer only (licence: NONE) · get_code("056e2e87a305e0a2") |
| conv2dTranspose | Ran | Owais-Ansari/Unet3plus/utils/models.py code served (permissive licence) · get_code("9d9f51c95a9339d6") |
| conv_block | Ran | hamidriasat/UNet-3-Plus/models/unet3plus_utils.py code served (permissive licence) · get_code("dc407cea8c628ee7") |
| dot_product | Ran | hamidriasat/UNet-3-Plus/models/unet3plus_utils.py code served (permissive licence) · get_code("f5be84a091639139") |
| double_conv | Ran | Owais-Ansari/Unet3plus/utils/models.py code served (permissive licence) · get_code("a505d8b9327be637") |
| focal_loss | Ran | hamidriasat/UNet-3-Plus/losses/loss.py code served (permissive licence) · get_code("7b23f6df494f23b1") |
| iou | Ran | hamidriasat/UNet-3-Plus/losses/loss.py code served (permissive licence) · get_code("c937560f46f9d96f") |
| iou_loss | Ran | hamidriasat/UNet-3-Plus/losses/loss.py code served (permissive licence) · get_code("013ffa32c7802109") |
| single_conv | Ran | Owais-Ansari/Unet3plus/utils/models.py code served (permissive licence) · get_code("3649cd96f92a4aa2") |
| prepare_model | Not yet run | hamidriasat/UNet-3-Plus/models/model.py code served (permissive licence) · get_code("0862fd72cdccf945") |
| unet3p_hybrid_loss | Not yet run | hamidriasat/UNet-3-Plus/losses/unet_loss.py code served (permissive licence) · get_code("7c0582a5703a92bd") |
| unet3plus | Not yet run | hamidriasat/UNet-3-Plus/models/unet3plus.py code served (permissive licence) · get_code("54e2be300f32a118") |
| unet3plus_backbone | Not yet run | hamidriasat/UNet-3-Plus/models/backbones.py code served (permissive licence) · get_code("407f5cbe72a47e63") |
| unet3plus_deepsup | Not yet run | hamidriasat/UNet-3-Plus/models/unet3plus_deep_supervision.py code served (permissive licence) · get_code("ab0d3102a089be66") |
| unet3plus_deepsup_cgm | Not yet run | hamidriasat/UNet-3-Plus/models/unet3plus_deep_supervision_cgm.py code served (permissive licence) · get_code("94663be587a24995") |
| vgg16_backbone | Not yet run | hamidriasat/UNet-3-Plus/models/backbones.py code served (permissive licence) · get_code("3ca7d63a43c639c4") |
| vgg19_backbone | Not yet run | hamidriasat/UNet-3-Plus/models/backbones.py code served (permissive licence) · get_code("16077947846367b1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation. Combining multi-scale features is one of important factors for accurate segmentation. UNet++ was developed as a modified Unet by designing an architecture with nested and dense skip connections. However, it does not explore sufficient information from full scales and there is still a large room for improvement. In this paper, we propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions. The full-scale skip connections incorporate low-level details with high-level semantics from feature maps in different scales; while the deep supervision learns hierarchical representations from the full-scale aggregated feature maps. The proposed method is especially benefiting for organs that appear at varying scales. In addition to accuracy improvements, the proposed UNet 3+ can reduce the network parameters to improve the computation efficiency. We further propose a hybrid loss function and devise a classification-guided module to enhance the organ boundary and reduce the over-segmentation in a non-organ image, yielding more accurate segmentation results. The effectiveness of the proposed method is demonstrated on two datasets. The code is available at: github.com/ZJUGiveLab/UNet-Version
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2004.08790")
get_code_for_paper("2004.08790")
have("2004.08790")
Connect an agent — have() is free.