Statistical Significance Analysis for SCEAF-UNet vs. Baseline Model

Results of a paired Wilcoxon signed-rank test comparing the performance of a baseline model and the SCEAF-UNet architecture for medical image segmentation. The dataset, 5.5 KB in size, was authored by Lingyun Zhao and last updated on March 25, 2026. It is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Validate the statistical significance of performance improvements in a new neural network architecture based on the reported test results.
• Compare model performance metrics between a baseline and an enhanced UNet variant based on the analysis.
• Inform the design of ablation studies or further experiments based on the significance findings mentioned in the description.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Has a clear, permissive license (CC-BY-4.0).
• Last update timestamp is precise (2026-03-25 18:00:13).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Lingyun Zhao.

Collection Method

Likely generated from ablation experiments on medical image segmentation models.

Time Range

Analysis date unspecified; dataset updated March 2026.

Freshness

Last updated 2026-03 25 18:00:13.

Geography

Spatial coverage is not specified.