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Models/SKIDv3

SKIDv3

Reported on 8 benchmarks across 1 task · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology8 results

  • Multi-Label ClassificationonMRNet
    AUC on ACL Tear (ACL)· 2021-04-21
    0.893
    best: 0.915 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    AUC on Abnormality (ABN)· 2021-04-21
    0.904
    best: 0.944 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    AUC on Meniscus Tear (MEN)· 2021-04-21
    0.81
    best: 0.822 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    Accuracy on ACL Tear (ACL)· 2021-04-21
    0.8
    best: 0.867 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    Accuracy on Abnormality (ABN)· 2021-04-21
    0.874
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    Accuracy on Meniscus Tear (MEN)· 2021-04-21
    0.725
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    Average AUC· 2021-04-21
    0.869
    best: 0.894 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481
  • Multi-Label ClassificationonMRNet
    Average Accuracy· 2021-04-21
    0.799
    best: 0.814 (MRNet)
    SOTA
    SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataarXiv:2104.10481