Ablation Results Comparing CAMBlock and ConvNeXt for Medical Image Segmentation

Ablation experiment results from a study proposing the SCEAF-UNet architecture for medical image segmentation. The 5.5 KB XLS file, authored by Lingyun Zhao and last updated in March 2026, contains data comparing the proposed CAMBlock module against ConvNeXt. Experiments were conducted on the public Synapse and ACDC datasets to validate the module's effectiveness.

Use Cases

• Benchmarking channel attention mechanisms based on the described CAMBlock module.
• Evaluating spatial-channel fusion techniques based on the SCEAF module's gated weighting.
• Comparing architectural components for medical image segmentation based on ablation study results.
• Validating edge enhancement modules based on the described EAF module for organ border delineation.

Strengths

• Data is from ablation experiments conducted on two public medical image datasets (Synapse and ACDC).
• File size is 5.5 KB, indicating a compact and focused results dataset.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality and structure require manual inspection.
• The dataset's small scale (5.5 KB) suggests it contains summary results, not raw image data.

Provenance

Source

figshare

Collection Method

Results from ablation experiments in a medical image segmentation research paper.

Time Range

Experiments likely conducted prior to the 2026 publication date.

Freshness

Last updated 2026-03-25 18:00:12; freshness should be verified.

Geography

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