CT-DegradBench is a new benchmark dataset for CT degradation detection and severity estimation, with the SeSpeCT framework using semantic priors from vision-language models and frequency features to predict artifact types and levels without fine-tuning.
Branch Contribution Analysis Table 4 evaluates the contribution of each branch in the pro- posed semantic-spectral framework
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CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
CT-DegradBench is a new benchmark dataset for CT degradation detection and severity estimation, with the SeSpeCT framework using semantic priors from vision-language models and frequency features to predict artifact types and levels without fine-tuning.