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A Segmentation Framework of Pulmonary Nodules in Lung CT Images (vol 29, pg 86, 2016)

JOURNAL OF DIGITAL IMAGING (2016)

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JOURNAL OF DIGITAL IMAGING

Volume 29, Issue 1, Pages 148-148

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SPRINGER

DOI: 10.1007/s10278-015-9812-6

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ResDSda_U-Net: A Novel U-Net-Based Residual Network for Segmentation of Pulmonary Nodules in Lung CT Images

Zhanlin Ji, Ziheng Zhao, Xinyi Zeng, Jingkun Wang, Li Zhao, Xueji Zhang, Ivan Ganchev

Summary: This paper proposes a novel neural network called ResDSda_U-Net, which combines DO-Conv and SimAM in the decoder, incorporates a denser DASPP module, and adds CCA and CSA blocks to improve the segmentation of pulmonary nodules.

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Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning

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PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

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Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images

Feng Shi, Bojiang Chen, Qiqi Cao, Ying Wei, Qing Zhou, Rui Zhang, Yaojie Zhou, Wenjie Yang, Xiang Wang, Rongrong Fan, Fan Yang, Yanbo Chen, Weimin Li, Yaozong Gao, Dinggang Shen

Summary: In this study, a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis is proposed. The experimental results demonstrate superior diagnosis performance and potential clinical application.

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Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images

Yifan Wang, Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Aamer Chughtai, Ella A. Kazerooni

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CE-NC-VesselSegNet: Supervised by contrast-enhanced CT images but utilized to segment pulmonary vessels from non-contrast-enhanced CT images

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A Segmentation Framework of Pulmonary Nodules in Lung CT Images (vol 29, pg 86, 2016)

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