GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
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TL;DR - GRIPNet is a real-time CT detector that incorporates the Gaussian-like radial intensity profile of pulmonary nodules into its architecture. It substantially improves small-nodule detection and localization across three public benchmarks, potentially strengthening early lung-cancer screening.
- Analysis of 18,218 annotated lesions found that nodule intensity typically peaks centrally and decays radially, with mean R² above 0.86 across datasets and size groups.
- Pinwheel convolutions model radial gradients, while a dual-frequency module separates boundary details from broader structural context.
- Dilated masked attention captures the spatial extent of intensity decay, and an adaptive loss emphasizes samples based on conspicuity.
- GRIPNet achieved mAP@0.5 of 95.3% on KanserSet, 91.6% on LUNA16, and 97.9% on Lung-PET-CT-Dx, with improved high-IoU localization at real-time speed.
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GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
Public signals
N/A
TL;DR - GRIPNet is a real-time CT detector that incorporates the Gaussian-like radial intensity profile of pulmonary nodules into its architecture. It substantially improves small-nodule detection and localization across three public benchmarks, potentially strengthening early lung-cancer screening.
- Analysis of 18,218 annotated lesions found that nodule intensity typically peaks centrally and decays radially, with mean R² above 0.86 across datasets and size groups.
- Pinwheel convolutions model radial gradients, while a dual-frequency module separates boundary details from broader structural context.
- Dilated masked attention captures the spatial extent of intensity decay, and an adaptive loss emphasizes samples based on conspicuity.
- GRIPNet achieved mAP@0.5 of 95.3% on KanserSet, 91.6% on LUNA16, and 97.9% on Lung-PET-CT-Dx, with improved high-IoU localization at real-time speed.