arXiv Computer Vision
From Slices to Volumes: Comparing 2D, 2.5D, and 3D Residual Networks for Pulmonary Nodule Malignancy-Risk Classification
Read the original on arXiv Computer Vision →The study compares 2D, 2.5D, and 3D residual networks for classifying pulmonary nodule malignancy risk using a retrospective LIDC‑IDRI cohort of 2,654 nodules. Models were trained with random initialization, momentum‑contrast pretraining, and mild data augmentation, and evaluated on patient‑disjoint splits. Augmented 2.5D models achieved the highest test Macro‑F1 (0.6537) and accuracy (0.6869), while 2D and 3D models performed lower, and pretraining generally reduced performance.
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