Discriminator network.

<div><p>Accurate segmentation of pulmonary nodules plays a critical role in monitoring disease progression and enabling early lung cancer screening. However, this task remains challenging due to the complex morphological variability of pulmonary nodules in CT images and the limited avail...

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Bibliographic Details
Main Author:	Yong Wang (12837) (author)
Other Authors:	Seri Mastura Mustaza (22086461) (author), Mohammad Syuhaimi Ab-Rahman (22086464) (author), Siti Salasiah Mokri (22086467) (author)
Published:	2025
Subjects:	Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified traditional 3d methods slice spatial information reducing computational requirements preserving spatial information monitoring disease progression mitigate mode collapse luna16 dataset demonstrated incorporate spatial continuity improve model robustness convolutional neural network complex morphological variability channel attention mechanism approach extracted features enhance segmentation precision accurate segmentation across scale feature maps improved model stability generated results align better capture edge edge segmentation accuracy pulmonary nodules plays incorporated minibatch discrimination color ct images pulmonary nodules minibatch discrimination ct images segmentation performance nested edge generated samples feature matching edge regions also improved wide range statistical characteristics side outputs rgb channels real data proposed method loss function limited availability level details generator adopted gan framework fuse inter encouraging diversity embedded within deep supervision critical role constructed pseudo annotated datasets
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Discriminator network.

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