Methods › Computer Vision › Semantic Segmentation Models › EfficientUNet++
EfficientUNet++
Introduced by João Lourenço Silva et al. in Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Decoder architecture inspired on the UNet++ structure and the EfficientNet building blocks. Keeping the UNet++ structure, the EfficientUNet++ achieves higher performance and significantly lower computational complexity through two simple modifications:
- Replaces the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions
- Applies channel and spatial attention to the bottleneck feature maps using concurrent spatial and channel squeeze & excitation (scSE) blocks
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation 21 Jun 2021 · 1 repository · arXiv:2106.11447
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Coronary Artery Segmentation | 1 |
| Decoder | 1 |
| Lesion Detection | 1 |
| Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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