AINeutralarXiv – CS AI · 10h ago6/10
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CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
Researchers developed CT-IDP, a quantitative phenotyping framework that uses organ segmentation and derived descriptors to classify abdominal CT diseases through interpretable logistic regression. The approach achieved superior performance compared to vision-transformer baselines across multiple datasets, demonstrating the value of explainable AI in medical imaging.