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Models/DCNN-based MoE

DCNN-based MoE

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

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

Methodology3 results

Anomaly DetectiononHistoArtifacts
1:1 Accuracy· 2024-03-12
97.82
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743
Anomaly DetectiononHistoArtifacts
Avg F1· 2024-03-12
86.15
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743
Anomaly DetectiononHistoArtifacts
Recall/ Sensitivity· 2024-03-12
97.93
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743

Computer Vision3 results

3D Anomaly DetectiononHistoArtifacts
1:1 Accuracy· 2024-03-12
97.82
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743
3D Anomaly DetectiononHistoArtifacts
Avg F1· 2024-03-12
86.15
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743
3D Anomaly DetectiononHistoArtifacts
Recall/ Sensitivity· 2024-03-12
97.93
SOTA
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs arXiv:2403.07743