Multiple Instance Learning Classification on TIGER
96.9AccuracyASMIL
Evaluation Results
| Method | Links | |
|---|---|---|
| ASMIL2026.03 | 96.9 | |
| DEMIL2026.03 | 96.5 | |
| TransMIL2026.03 | 96.3 | |
| TG-RGMIL [0]DTopological features=0D2023.07 | 96.1 | |
| TG-RGMIL [0,1,2]DTopological features=0D, 1D, 2D2023.07 | 95.7 | |
| TG-RGMIL [0,1]DTopological features=0D, 1D2023.07 | 95.3 | |
| ABMIL2026.03 | 95.3 | |
| DSMIL2026.03 | 95.1 | |
| RGMIL2026.03 | 94.9 | |
| PSMIL2026.03 | 94.7 | |
| GAPMILwD (2025)Reference Year=20252023.07 | 91.9 | |
| APMILwD (2025)Reference Year=20252023.07 | 91.7 | |
| DPMIL (2018)Reference Year=20182023.07 | 89.7 | |
| GNNMIL (2019)Reference Year=20192023.07 | 87.6 | |
| DSMIL (2021)Reference Year=20212023.07 | 86.9 | |
| BDRMIL (2022)Reference Year=20222023.07 | 86.9 | |
| DistNet (2023)Reference Year=20232023.07 | 86.4 | |
| miGraph2016.10 | 86 | |
| ALP-SVM2016.10 | 86 | |
| GAPMIL (2018)Reference Year=20182023.07 | 84.5 | |
| MI-ANFIS2016.10 | 84.5 | |
| RGMIL (2023)Reference Year=20232023.07 | 84.2 | |
| MI-kernel2016.10 | 84.2 | |
| MI-SVM2016.10 | 84 | |
| APMIL (2018)Reference Year=20182023.07 | 83.9 | |
| MIForest2016.10 | 82 | |
| MIGraph2016.10 | 81.9 | |
| PPPM-kernel2016.10 | 80.2 | |
| mi-SVM2016.10 | 78.4 | |
| EM-DD2016.10 | 72.1 |