首页|False positive reduction for lung nodule detection using 3D Channel-Spatial attention model with multi-descriptor-based refinement
False positive reduction for lung nodule detection using 3D Channel-Spatial attention model with multi-descriptor-based refinement
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NETL
NSTL
Elsevier
Reducing false positives is crucial in lung nodule detection since an excessive false positive can challenge radiologists' interpretation. In this research, we collected the medical exam (ME) dataset, whose diagnostic imaging is characterized by small nodules (<6mm) and blurriness (ground glass opacity, GGO). To account for overall sensitivity after false-positive reduction, we propose a 3D multi-scale channel-spatial attention model with multi-descriptor-based refinement, consisting of three sub-networks: the 3D multi-scale channel-spatial attention (MCSA), the 3D cross-attention, and the multi-descriptor-based classification (MDC). The 3D MCSA primarily comprises 3D multi-scale zoom-in and zoom-out streams with a channel-spatial attention module to enhance the quality of low-gradient nodules and facilitate feature extraction from diverse receptive fields, while the 3D cross-attention fuses feature maps from diverse receptive fields and generates the feature descriptor. The MDC utilizes multi-feature descriptors for the final 3D candidate nodule decision. Our model achieves sensitivities of 84.0 % and 97.3 % on the ME and LUNA16 candidate datasets under 2 FPs/scan, respectively.
NETL
NSTL
Elsevier
