Results of embedding the SCEAF module at different decoding stages, where D4 to D1 represent the deepest to shallowest stages of the decoder, respectively. The dataset was published by Lingyun Zhao on figshare in March 2026 under a CC-BY-4.0 license. The file is 5.5 KB in size and is available in XLS format.
Use Cases
- Conducting ablation studies to understand the impact of feature fusion at different network depths based on the described decoder stages.
- Evaluating the performance of the SCEAF module's gated weighting and adaptive fusion mechanisms in medical image segmentation.
- Benchmarking segmentation architectures on public datasets like Synapse and ACDC, as indicated by the platform tags.
- Analyzing how skip connections and channel feature representations affect edge information and structural contour capture.
Strengths
- Dataset is openly licensed under CC-BY-4.0, facilitating reuse and redistribution.
- File size is 5.5 KB, ensuring quick download and easy inspection.
- Data was last updated on 2026-03-25, indicating recent availability.
Limitations
- Row count and column definitions are unknown, which limits suitability assessment.
- Description metadata is limited; actual data quality and structure require manual inspection after download.
- The dataset's small size (5.5 KB) suggests it contains summary or experimental results, not raw image data.
Provenance
- Source
- figshare
- Collection Method
- Likely contains experimental results from ablation studies on a neural network module.
- Time Range
- Publication date is March 2026.
- Freshness
- Last updated 2026-03-25 18:00:11; freshness should be verified.
- Geography
- null