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Article: A Joint Detection and Recognition Approach to Lung Cancer Diagnosis From CT Images With Label Uncertainty

TitleA Joint Detection and Recognition Approach to Lung Cancer Diagnosis From CT Images With Label Uncertainty
Authors
KeywordsDeep learning
multi-task learning
nodule detection
nodule malignancy classification
label noise
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2020, v. 8, p. 228905-228921 How to Cite?
AbstractAutomatic lung cancer diagnosis from computer tomography (CT) images requires the detection of nodule location as well as nodule malignancy prediction. This article proposes a joint lung nodule detection and classification network for simultaneous lung nodule detection, segmentation and classification subject to possible label uncertainty in the training set. It operates in an end-to-end manner and provides detection and classification of nodules simultaneously together with a segmentation of the detected nodules. Both the nodule detection and classification subnetworks of the proposed joint network adopt a 3-D encoder-decoder architecture for better exploration of the 3-D data. Moreover, the classification subnetwork utilizes the features extracted from the detection subnetwork and multiscale nodule-specific features for boosting the classification performance. The former serves as valuable prior information for optimizing the more complicated 3D classification network directly to better distinguish suspicious nodules from other tissues compared with direct backpropagation from the decoder. Experimental results show that this co-training yields better performance on both tasks. The framework is validated on the LUNA16 and LIDC-IDRI datasets and a pseudo-label approach is proposed for addressing the label uncertainty problem due to inconsistent annotations/labels. Experimental results show that the proposed nodule detector outperforms the state-of-the-art algorithms and yields comparable performance as state-of-the-art nodule classification algorithms when classification alone is considered. Since our joint detection/recognition approach can directly detect nodules and classify its malignancy instead of performing the tasks separately, our approach is more practical for automatic cancer and nodules detection.
Persistent Identifierhttp://hdl.handle.net/10722/307870
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 0.960
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLIU, C-
dc.contributor.authorChan, SC-
dc.date.accessioned2021-11-12T13:39:06Z-
dc.date.available2021-11-12T13:39:06Z-
dc.date.issued2020-
dc.identifier.citationIEEE Access, 2020, v. 8, p. 228905-228921-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/307870-
dc.description.abstractAutomatic lung cancer diagnosis from computer tomography (CT) images requires the detection of nodule location as well as nodule malignancy prediction. This article proposes a joint lung nodule detection and classification network for simultaneous lung nodule detection, segmentation and classification subject to possible label uncertainty in the training set. It operates in an end-to-end manner and provides detection and classification of nodules simultaneously together with a segmentation of the detected nodules. Both the nodule detection and classification subnetworks of the proposed joint network adopt a 3-D encoder-decoder architecture for better exploration of the 3-D data. Moreover, the classification subnetwork utilizes the features extracted from the detection subnetwork and multiscale nodule-specific features for boosting the classification performance. The former serves as valuable prior information for optimizing the more complicated 3D classification network directly to better distinguish suspicious nodules from other tissues compared with direct backpropagation from the decoder. Experimental results show that this co-training yields better performance on both tasks. The framework is validated on the LUNA16 and LIDC-IDRI datasets and a pseudo-label approach is proposed for addressing the label uncertainty problem due to inconsistent annotations/labels. Experimental results show that the proposed nodule detector outperforms the state-of-the-art algorithms and yields comparable performance as state-of-the-art nodule classification algorithms when classification alone is considered. Since our joint detection/recognition approach can directly detect nodules and classify its malignancy instead of performing the tasks separately, our approach is more practical for automatic cancer and nodules detection.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rightsIEEE Access. Copyright © Institute of Electrical and Electronics Engineers: Open Access Journals.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDeep learning-
dc.subjectmulti-task learning-
dc.subjectnodule detection-
dc.subjectnodule malignancy classification-
dc.subjectlabel noise-
dc.titleA Joint Detection and Recognition Approach to Lung Cancer Diagnosis From CT Images With Label Uncertainty-
dc.typeArticle-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2020.3044941-
dc.identifier.scopuseid_2-s2.0-85099057268-
dc.identifier.hkuros329432-
dc.identifier.volume8-
dc.identifier.spage228905-
dc.identifier.epage228921-
dc.identifier.isiWOS:000604510000001-
dc.publisher.placeUnited States-

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