Model card
STU-Net-L.
Ziyan Huang et al.open-source440M paramsScalable U-Net
Scalable and transferable U-Net pre-trained on TotalSegmentator dataset. Large variant (440M params).
§ 01 · Benchmarks
Every benchmark STU-Net-L has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | BTCV | Medical · Medical Image Segmentation | mean-dsc | 83.5% | #2 | 2023-04-13 | source ↗ |
| 02 | BraTS 2023 | Medical · Medical Image Segmentation | mean-dice-wt-tc-et | 0.9% | #2 | 2023-04-13 | source ↗ |
| 03 | Synapse Multi-Organ CT | Medical · Medical Image Segmentation | mean-dsc | 84.2% | #5 | 2023-04-13 | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 03 · Papers
1 paper with results for STU-Net-L.
- 2023-04-13· Medical· 3 results
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
§ 04 · Related models
Other Ziyan Huang et al. models scored on Codesota.
§ 05 · Sources & freshness
Where these numbers come from.
arxiv
3
results
3 of 3 rows marked verified.