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Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang
2022 Diagnostics  
Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture.  ...  Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification.  ...  General Neural Network Architecture Most published studies combined the conventional convolution network (CNN) architecture with neural network blocks for lung nodule segmentation.  ... 
doi:10.3390/diagnostics12020298 pmid:35204388 pmcid:PMC8871398 fatcat:zbasqznr5vblnkfmeuzwlmqbom

HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

Ling Zhu, Hongqing Zhu, Suyi Yang, Pengyu Wang, Yang Yu
2021 EURASIP Journal on Image and Video Processing  
In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme.  ...  To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function.  ...  For instance, CoLe-CNN [8] accessed the context information of nodules by generating two masks of all background and secondary elements, and introduced an asymmetric loss function that could automatically  ... 
doi:10.1186/s13640-021-00574-2 fatcat:wk343vxunfe2npnbxl5gohoaqe