Multi-rater Medical Image Segmentation on LIDC-IDRI (test)
0.1358GEDD-Persona
Evaluation results
| Method | Links | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| D-PersonaFramework Stage=I, Sampling number=502024.03 | 0.1358 | 90.45 | 91.37 | 91.33 | — | — | — | — | — | |
| D-PersonaFramework Stage=I, Sampling number=302024.03 | 0.1375 | 90.42 | 91.23 | 91.16 | — | — | — | — | — | |
| Harmonizer Network2026.05 | 0.1419 | 91.35 | 92.65 | 90 | 89.87 | 90.07 | 91.06 | 88.56 | 90.78 | |
| D-PersonaFramework Stage=II2024.03 | 0.1444 | 90.31 | 90.38 | 89.17 | 88.54 | 89.5 | 90.03 | 88.6 | 89.17 | |
| D-Persona2026.05 | 0.1444 | 90.31 | 90.38 | 89.17 | 88.54 | 89.5 | 90.03 | 88.6 | 89.17 | |
| D-PersonaFramework Stage=I, Sampling number=102024.03 | 0.1461 | 90.24 | 90.75 | 90.51 | — | — | — | — | — | |
| Pionono2024.03 | 0.1502 | 90 | 90.1 | 88.97 | 87.94 | 89.11 | 89.55 | 88.76 | 88.84 | |
| Pionono2026.05 | 0.1502 | 90 | 90.1 | 88.97 | 87.94 | 89.11 | 89.55 | 88.76 | 88.84 | |
| Prob. U-NetSampling number=502024.03 | 0.2168 | 88.8 | 88.87 | 88.81 | — | — | — | — | — | |
| Prob. U-Net2026.05 | 0.2168 | 88.8 | 88.87 | 88.81 | — | — | — | — | — | |
| Prob. U-NetSampling number=302024.03 | 0.2169 | 88.79 | 88.8 | 88.73 | — | — | — | — | — | |
| Prob. U-NetSampling number=102024.03 | 0.2181 | 88.79 | 88.6 | 88.43 | — | — | — | — | — | |
| TAB2024.03 | 0.2322 | 86.35 | 87.11 | 86.08 | 85 | 86.35 | 86.77 | 85.77 | 85.97 | |
| TAB2026.05 | 0.2322 | 86.35 | 87.11 | 86.08 | 85 | 86.35 | 86.77 | 85.77 | 85.97 | |
| CM-Pixel2024.03 | 0.2407 | 88.64 | 87.72 | 87.72 | 85.99 | 88.81 | 89.31 | 86.77 | 87.72 | |
| CM-Pixel2026.05 | 0.2407 | 88.64 | 87.72 | 87.72 | 85.99 | 88.81 | 89.31 | 86.77 | 87.72 | |
| CM-Global2024.03 | 0.2432 | 88.53 | 87.51 | 87.51 | 86.13 | 88.76 | 88.99 | 86.18 | 87.51 | |
| CM-Global2026.05 | 0.2432 | 88.53 | 87.51 | 87.51 | 86.13 | 88.76 | 88.99 | 86.18 | 87.51 | |
| U-NetExpert used for training=A32024.03 | 0.2436 | 88.2 | — | — | 85.29 | 88.48 | 89.4 | 87.2 | 87.59 | |
| U-Net (A3)Training=single-expert annotations (A3)2026.05 | 0.2436 | 88.2 | — | — | 85.29 | 88.48 | 89.4 | 87.2 | 87.59 | |
| U-NetExpert used for training=A22024.03 | 0.2459 | 88.43 | — | — | 87.16 | 89.08 | 88.59 | 85.15 | 87.5 | |
| U-Net (A2)Training=single-expert annotations (A2)2026.05 | 0.2459 | 88.43 | — | — | 87.16 | 89.08 | 88.59 | 85.15 | 87.5 | |
| U-NetExpert used for training=A42024.03 | 0.2962 | 85.83 | — | — | 80.8 | 85.48 | 88.22 | 88.9 | 85.85 | |
| U-Net (A4)Training=single-expert annotations (A4)2026.05 | 0.2962 | 85.83 | — | — | 80.8 | 85.48 | 88.22 | 88.9 | 85.85 | |
| U-NetExpert used for training=A12024.03 | 0.3062 | 86.59 | — | — | 87.8 | 87.47 | 85.49 | 80.67 | 85.36 | |
| U-Net (A1)Training=single-expert annotations (A1)2026.05 | 0.3062 | 86.59 | — | — | 87.8 | 87.47 | 85.49 | 80.67 | 85.36 | |
| Geometric-Structural2026.05 | — | — | — | — | 88.53 | 87.98 | 88.33 | 88.16 | 88.25 |