Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88798
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Gong, ZH | - |
dc.creator | Li, D | - |
dc.creator | Lin, JT | - |
dc.creator | Zhang, Y | - |
dc.creator | Lam, KM | - |
dc.date.accessioned | 2020-12-22T01:08:02Z | - |
dc.date.available | 2020-12-22T01:08:02Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88798 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication Gong, Z. H., Li, D., Lin, J. T., Zhang, Y., & Lam, K. M. (2020). Towards accurate pulmonary nodule detection by representing nodules as points with high-resolution network. IEEE Access, 8, 157391-157402 is available at https://dx.doi.org/10.1109/ACCESS.2020.3019104 | en_US |
dc.subject | Three-Dimensional displays | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Detectors | en_US |
dc.subject | Two dimensional displays | en_US |
dc.subject | Estimation | en_US |
dc.subject | Sensitivity | en_US |
dc.subject | Lung | en_US |
dc.subject | Lung nodule detection | en_US |
dc.subject | 3D convolution neural network | en_US |
dc.subject | Keypoint estimation | en_US |
dc.title | Towards accurate pulmonary nodule detection by representing nodules as points with high-resolution network | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 157391 | - |
dc.identifier.epage | 157402 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3019104 | - |
dcterms.abstract | Almost all successful nodule detectors rely heavily on a fixed set of anchor boxes. In this paper, inspired by the success of the keypoint estimation method in natural image detection, we propose an anchor-free framework for accurate pulmonary nodule detection. We first present a novel representation for detecting nodules, in terms of their 3D center locations, which reduces the number of hyper-parameters and the corresponding computation related to anchors, thus making the nodule detection pipeline much simpler. Then, an effective two-stream network is introduced to reduce the false positive nodule candidates, by aggregating information from the image stream and motion-history stream. Experiments show that the proposed approach achieves a sensitivity of 96.1%, with 8 false positives per scan, and a CPM score of 90.6%, on the publicly available LUNA16 dataset, which outperforms other state-of-the-art methods. By testing on the SPIE-AAPM dataset with models pre-trained on the LUNA16, our proposed method yields 92.8% sensitivity with 8 false positives per scan. This demonstrates the effectiveness and generalization ability of our method. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, . . 2020, , v. 8, p. 157391-157402 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000568267000001 | - |
dc.identifier.scopus | 2-s2.0-85091238033 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202012 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Gong_Accurate_Pulmonary_Nodule.pdf | 2.46 MB | Adobe PDF | View/Open |
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