Cardiac Phenotype Prediction on UK BioBank
17.103LVM (g)Mean-guess
Evaluation results
| Method | Links | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean-guessDescription=estimating every subject's phenotype value with the cohort mean value2024.06 | 17.103 | 12.14 | 6.586 | 31.915 | 8.918 | — | — | — | — | — | — | — | — | — | — | — | — | |
| MAE^u_kSampling strategy=Undersampled R=4, Backbone architecture=MAE, Input domain=k-space based2026.03 | 10.09 | 5.97 | 6.56 | 14.76 | 5.78 | 12.88 | 8.42 | 6.09 | 8.71 | 9.64 | 6.71 | 7.4 | — | — | — | — | — | |
| ViTInput format=concatenated 3D+T planes2024.06 | 8.656 | 4.292 | 6.59 | 14.102 | 8.944 | — | — | — | — | — | — | — | — | — | — | — | — | |
| RN50^u_kSampling strategy=Undersampled R=4, Backbone architecture=ResNet-50, Input domain=k-space based2026.03 | 8 | 3.66 | 6.53 | 11.33 | 5.84 | 9.57 | 7.03 | 3.58 | 7.63 | 8.41 | 5.74 | 8.01 | — | — | — | — | — | |
| ViTSampling strategy=Fully-sampled, Backbone architecture=ViT, Input domain=Image-based2026.03 | 7.85 | 4.17 | 6.09 | 13.75 | 5.55 | 11.53 | 6.93 | 3.91 | 8.4 | 9.51 | 5.24 | 7.44 | — | — | — | — | — | |
| k-MTRSampling strategy=Undersampled R=4, Input domain=k-space based2026.03 | 7.2 | 3.28 | 5.74 | 10.6 | 5.34 | 8.15 | 6.5 | 3.14 | 6.98 | 7.86 | 4.72 | 7.07 | — | — | — | — | — | |
| ResNet50Input format=concatenated 3D+T planes2024.06 | 7.15 | 3.245 | 6.294 | 12.577 | 5.777 | — | — | — | — | — | — | — | — | — | — | — | — | |
| RN50^uSampling strategy=Undersampled R=4, Backbone architecture=ResNet-50, Input domain=Image-based2026.03 | 7.01 | 3.56 | 5.55 | 10.49 | 5.41 | 7.35 | 6.55 | 3.3 | 7.35 | 8.06 | 4.82 | 7.08 | — | — | — | — | — | |
| MAESampling strategy=Fully-sampled, Backbone architecture=MAE, Input domain=Image-based2026.03 | 5.87 | 3.62 | 5.05 | 9.49 | 4.1 | 9.03 | 5.86 | 3.2 | 6.07 | 7.42 | 4.29 | 5.47 | — | — | — | — | — | |
| RN50Sampling strategy=Fully-sampled, Backbone architecture=ResNet-50, Input domain=Image-based2026.03 | 5.51 | 3.3 | 5.09 | 9.24 | 4.66 | 6.58 | 5.68 | 2.95 | 6.15 | 7.58 | 4.28 | 5.8 | — | — | — | — | — | |
| Whole Heart 3D+T Representation Learning2024.06 | 4.332 | 2.831 | 5.396 | 10.713 | 3.529 | — | — | — | — | — | — | — | — | — | — | — | — | |
| COF2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 16.77 | 0.123 | 17.36 | 14.4 | 2.31 | |
| COF-w/Diff.Variation=with Diffusion2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 62.07 | 0.476 | 64.97 | 34.92 | 6.95 | |
| ConsistI2V2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 50.54 | 5.606 | 73.11 | 86.4 | 11.91 | |
| EchoDiffusion2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 33.17 | 0.531 | 76.42 | 33.38 | 14.7 | |
| EchoNet-Syn.2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 44.03 | 20.32 | 88.44 | 70.37 | 13.55 | |
| ECHOPulse2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 46.39 | 0.997 | 56.58 | 76.66 | 9.24 | |
| LFDM2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 35.27 | 2.087 | 73.46 | 65.77 | 13.72 | |
| MoFA-Video2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 32.18 | 0.217 | 33.32 | 19.67 | 2.07 | |
| X-Dyna2026.02 | — | — | — | — | — | — | — | — | — | — | — | — | 35.57 | 0.236 | 37.04 | 22.47 | 2.05 |