Prostate cancer lesion detection on Prostate cancer PET/CT (test)
81Mean TPAttention U-Net
Evaluation results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| Attention U-NetLoss Function=DL2025.02 | 81 | 2.06 | 19 | 50 | — | — | — | |
| SegResNetLoss Function=DFL2025.02 | 81 | 2.05 | 19 | 49 | — | — | — | |
| SAM-Med3DLoss Function=Zero-shot2025.02 | 78 | 2.32 | 33 | 53 | — | — | — | |
| SAM-Med3DLoss Function=L1DFL2025.02 | 78 | 1.55 | 33 | 69 | — | — | — | |
| Attention U-NetLoss Function=DFL2025.02 | 77 | 2.65 | 23 | 44 | — | — | — | |
| Attention U-NetLoss Function=L1DFL2025.02 | 77 | 0.42 | 23 | 69 | — | — | — | |
| SegResNetLoss Function=L1DFL2025.02 | 76 | 0.52 | 24 | 66 | — | — | — | |
| SAM-Med3DLoss Function=DL2025.02 | 75 | 0.9 | 31 | 70 | — | — | — | |
| SAM-Med3DLoss Function=Finetuned (DCE)2025.02 | 74 | 0.45 | 29 | 71 | — | — | — | |
| SegResNetLoss Function=DL2025.02 | 73 | 0.73 | 27 | 62 | — | — | — | |
| SAM-Med3DLoss Function=DFL2025.02 | 73 | 0.64 | 29 | 71 | — | — | — | |
| U-NetLoss Function=DFL2025.02 | 71 | 1.66 | 28 | 46 | — | — | — | |
| U-NetLoss Function=L1DFL2025.02 | 71 | 0.71 | 29 | 58 | — | — | — | |
| U-NetLoss Function=DL2025.02 | 69 | 1.27 | 30 | 49 | — | — | — | |
| UNETRLoss Function=DL2025.02 | 64 | 7.74 | 33 | 21 | — | — | — | |
| UNETRLoss Function=DFL2025.02 | 64 | 5.49 | 32 | 26 | — | — | — | |
| UNETRLoss Function=L1DFL2025.02 | 61 | 2.59 | 35 | 38 | — | — | — | |
| FDG source modelAdaptation strategy=no adapt2026.03 | — | — | — | — | 22.5 | 18.4 | 12.8 | |
| Fine-tuned modelSupervision type=labeled PSMA2026.03 | — | — | — | — | 72.9 | 60.5 | 51.8 | |
| Proposed Self-training UDAAdaptation strategy=UDA adapted2026.03 | — | — | — | — | 58.2 | 46.7 | 40.5 | |
| Proposed Self-training UDAAdaptation strategy=UDA+anchor adapt2026.03 | — | — | — | — | 60.7 | 47.2 | 40.8 | |
| Proposed Self-training UDAAdaptation strategy=UDA+label shift2026.03 | — | — | — | — | 61.3 | 48.4 | 41.7 |