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Cited by 491

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network

Open access
Liao, Fangzhou, Liang, Ming, Li, Zhe, Hu, Xiaolin, Song, Sen·2019·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2019.2892409
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Abstract

Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-OR gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.

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Show all 13 references

Research works printed but not linked: 30

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Metadata

Authors
Liao, F., Liang, M., Li, Z., Hu, X., & Song, S.
Venue
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-11
Volume
30
Issue
11
Pages
3484-3495
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
DOI
10.1109/tnnls.2019.2892409
OpenAlex
W2769848455
Cited by
491
References
13
FWCI
34.42
Type
journal article
Language
en
Open access
green
Licence
Funders
National Natural Science Foundation of China (61836014, 91420201, 61621136008, 61620106010)

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CoChat. (2026). Citation check: Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. https://app.cochat.ai/papers/10.1109/tnnls.2019.2892409
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Liao, F., Liang, M., Li, Z., Hu, X., & Song, S. (2019). Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2019.2892409

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.