3D-Morphomics, Morphological Features on CT Scans for Lung Nodule Malignancy Diagnosis-Reference-Cited by-同舟云学术

3D-Morphomics, Morphological Features on CT Scans for Lung Nodule Malignancy Diagnosis

Published:2022 Issue: Volume: Page:3-13
ISSN:0302-9743
Container-title:Cancer Prevention Through Early Detection
language:
Short-container-title:

Author:

Munoz Elias,Baudot Pierre,Le Van-Khoa,Voyton Charles,Renoust Benjamin,Francis Danny,Groza Vladimir,Brisset Jean-Christophe,Geremia Ezequiel,Iannessi Antoine,Liu Yan,Huet Benoit

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-17979-2_1

Reference30 articles.

1. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395–409 (2011). https://doi.org/10.1056/NEJMoa1102873

2. Lung nodule classification on LIDC-IDRI challenge (2022). https://paperswithcode.com/sota/lung-nodule-classification-on-lidc-idri

3. Aerts, H.J., et al.: Corrigendum: decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5(1), 4006 (2014). https://doi.org/10.1038/ncomms5006

4. Al-Shabi, M., Lan, B.L., Chan, W.Y., Ng, K.H., Tan, M.: Lung nodule classification using deep local-global networks. Int. J. Comput. Assist. Radiol. Surg. 14(10), 1815–1819 (2019)

5. Al-Shabi, M., Lee, H.K., Tan, M.: Gated-dilated networks for lung nodule classification in CT scans. IEEE Access 7, 178827–178838 (2019). https://doi.org/10.1109/ACCESS.2019.2958663

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification;Medical Physics;2023-02