MELD

Reported on 5 benchmarks across 5 tasks · 1 paper · 5 SOTA

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

Methodology4 results

3DonDeepLesion
Sensitivity· uses extra data· 2020-05-28
86.6
best: 88.55 (P3D)
SOTA
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets arXiv:2005.13753
2D ClassificationonDeepLesion
Sensitivity· uses extra data· 2020-05-28
86.6
best: 88.55 (P3D)
SOTA
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets arXiv:2005.13753
2D Object DetectiononDeepLesion
Sensitivity· uses extra data· 2020-05-28
86.6
best: 88.55 (P3D)
SOTA
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets arXiv:2005.13753
16konDeepLesion
Sensitivity· uses extra data· 2020-05-28
86.6
best: 88.55 (P3D)
SOTA
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets arXiv:2005.13753

Computer Vision1 result

Object DetectiononDeepLesion
Sensitivity· uses extra data· 2020-05-28
86.6
best: 88.55 (P3D)
SOTA
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets arXiv:2005.13753