Data Sheet 1_Frequency response compensation to optimize pretrained deep learning models in lung nodule malignancy classification.docx

The compensation filters are fixed, thereby avoiding additional parameter learning within the filtering operation. Patient-level data partitioning was used to ensure that nodules from the same patient were not distributed across different experimental subsets, reducing the risk of information leakage.</p>Results<p>The proposed model achieved an accuracy, sensitivity, and specificity of 0.90, an F1-score of 0.81, and an AUROC of 0.95 on the held-out patient-level test set.

Performance was additionally assessed using patient-level cross-validation, cross imbalance analysis, an IQ-OTH/NCCD image-level evaluation and an independent BUS-BRA patient-level evaluation. The experimental analysis further included statistical significance testing using McNemar’s test for pairwise model comparisons, followed by the Holm correction to control for multiple comparisons, while Cochran’s Q test was employed to assess performance differences across multiple model configurations.

The combined configuration achieved the best sensitivity and balanced accuracy relative to the branch-wise variants. The Bilinear Lag-Lead (BT-LL) framework thus provides an explicit, frequency response mechanism for integrating contributions of low- and high-frequency information in pulmonary nodule representations.</p>