Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10278-024-01004-1.pdf
Reference19 articles.
1. Singadkar G, Mahajan A, Thakur M, Talbar S: Deep deconvolutional residual network based automatic lung nodule segmentation. Journal of digital imaging 33:678-684, 2020
2. Zhang Q, Wang H, Yoon SW, Won D, Srihari, K: Lung nodule diagnosis on 3D computed tomography images using deep convolutional neural networks. Procedia Manufacturing, 39:363-370, 2019
3. Aresta G, Jacobs C, Araújo T, Cunha A, Ramos I, van Ginneken B, Campilho A: iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Scientific reports, 9(1):1-9, 2019
4. Ren H, Zhou L, Liu G, Peng X, Shi W, Xu H, Shan F, Liu L: An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing. Quantitative Imaging in Medicine and Surgery 10(1):233, 2020
5. Cao H, Liu H, Song E, Hung CC, Ma G, Xu X, Jin R, Lu J: Dual-branch residual network for lung nodule segmentation. Applied Soft Computing 86:105934, 2020
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