Lung Nodule Segmentation: Exploring Data Efficiency and Advanced Architectures

원문 초록

This study explores the application of various deep learning models for the segmentation of lung nodules using LIDC- IDRI. Unlike traditional approaches that utilize the full dataset, our work emphasizes the efficacy of training models on a filtered subset of 356 samples. Novel configurations, including attention mechanisms and advanced preprocessing strategies, were em- ployed to optimize segmentation accuracy. Among the models evaluated, the DPLinkNet50 with a Channel Attention Bridge and ResNet backbone demonstrated the highest performance with a Dice score of 0.86 and percision 0.88, significantly outper- forming conventional architectures. This work underscores the potential of leveraging data efficiency and tailored architectures in achieving robust segmentation performance, paving the way for improved computer-aided diagnosis in clinical settings.