CVD prediction on Clinical Multimodal Dataset Core (test)
0.915AUROCMedM2T
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| MedM2TAblation Configuration=Full Model2025.10 | 0.915 | 0.632 | |
| MedM2TAblation Configuration=w/o Shared Encoder2025.10 | 0.909 | 0.628 | |
| MedM2TAblation Configuration=w/o Bi-Modal Attention2025.10 | 0.902 | 0.624 | |
| MedM2TAblation Configuration=w/o Pre-trained Encoder2025.10 | 0.901 | 0.604 | |
| HAIM2025.10 | 0.899 | 0.633 | |
| MultiBenchFusion Strategy=LRTF, Encoder Type=Original2025.10 | 0.897 | 0.621 | |
| MultiBenchFusion Strategy=LRTF, Encoder Type=Our Encoder2025.10 | 0.896 | 0.613 | |
| MultiBenchFusion Strategy=LF, Encoder Type=Original2025.10 | 0.895 | 0.617 | |
| MultiModNEncoder Type=Our Encoder2025.10 | 0.894 | 0.6 | |
| MultiBenchFusion Strategy=LF, Encoder Type=Our Encoder2025.10 | 0.89 | 0.583 | |
| MultiModNEncoder Type=Original2025.10 | 0.871 | 0.573 |