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End-to-end lung nodule detection framework with model-based feature projection block

2021-06-10 13:42:59
Ivan Drokin, Elena Ericheva
arXiv_CV
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.05741

PDF

https://arxiv.org/pdf/2106.05741.pdf

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