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Medical Image Classification on Mura s

79.57AccuracyLoRKD
70.709273.009675.3177.6104Apr 26, 2024

Evaluation results

MethodLinks
LoRKDPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0479.57
LoRKDPre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0478.33
LoRKDPre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0477.8
STLPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0477.26
MTLPre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0477.09
MTLPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.91
Aligned-MTLPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.91
MTL-KDPre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.91
MoCo-MTLPre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.91
Aligned-MTLPre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.9
BaselinePre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.73
STL-KDPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.73
BaselinePre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.73
BaselinePre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.73
Aligned-MTLPre-train=Med-MT, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.73
KFPre-train=Med-MT, Params(M)=1.60, Comp. Ratio=6.81%2024.0476.73
MTL-KDPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.55
FoundationPre-train=MedMnist, Params(M)=23.51, Comp. Ratio=/2024.0476.38
MTLPre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0476.19
MTL-KDPre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0475.84
MoCo-MTLPre-train=Radimagenet, Params(M)=1.25, Comp. Ratio=5.32%2024.0475.49
KFPre-train=Radimagenet, Params(M)=1.60, Comp. Ratio=6.81%2024.0474.96
MoCo-MTLPre-train=MedMnist, Params(M)=1.25, Comp. Ratio=5.32%2024.0474.95
KFPre-train=MedMnist, Params(M)=1.60, Comp. Ratio=6.81%2024.0474.77
FoundationPre-train=Med-MT, Params(M)=23.51, Comp. Ratio=/2024.0471.76
FoundationPre-train=Radimagenet, Params(M)=23.51, Comp. Ratio=/2024.0471.05