1 research outputs found
End-to-end lung nodule detection framework with model-based feature projection block
This paper proposes novel end-to-end framework for detecting suspicious
pulmonary nodules in chest CT scans. The method core idea is a new nodule
segmentation architecture with a model-based feature projection block on
three-dimensional convolutions. This block acts as a preliminary feature
extractor for a two-dimensional U-Net-like convolutional network. Using the
proposed approach along with an axial, coronal, and sagittal projection
analysis makes it possible to abandon the widely used false positives reduction
step. The proposed method achieves SOTA on LUNA2016 with 0.959 average
sensitivity, and 0.936 sensitivity if the false-positive level per scan is
0.25. The paper describes the proposed approach and represents the experimental
results on LUNA2016 as well as ablation studies