Model Performance Overview for Medical Imaging Tasks

5.5 KB Excel file containing model performance metrics for medical imaging tasks. The dataset, authored by Katharina V. Hoebel and last updated in April 2026, compares conventional and Monte Carlo dropout models using metrics like Spearman's rank correlation, AUROC, and MSE. Performance is evaluated on conditions including retinopathy of prematurity, knee osteoarthritis, and breast density classification.

Use Cases

• Benchmarking Monte Carlo dropout models against conventional models based on the performance comparison described.
• Analyzing model performance on ordinal medical classification tasks based on the described use of Spearman's rank correlation.
• Evaluating diagnostic model performance for retinopathy of prematurity based on the described AUROC measurement between normal/pre-plus and plus cases.
• Comparing classifier performance for knee osteoarthritis severity based on the described AUROC measurement between none/doubtful and mild/moderate/severe cases.

Strengths

• 5.5 KB file size enables quick download and inspection.
• Includes statistical significance indicators (p-values) for model comparisons.
• Performance metrics are calculated for three distinct medical imaging tasks.

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

Collection Method

Likely contains results from a model evaluation study.

Time Range

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Freshness

Last updated 2026-04-17 17:54:57.

Geography

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