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Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review
2022
Diagnostics
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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
2021
EURASIP Journal on Image and Video Processing
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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