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AuthorZhu, Hongbo
AuthorHan, Guangjie
AuthorLin, Chuan
AuthorWang, Min
AuthorGuizani, Mohsen
AuthorHou, Jianxia
AuthorXing, Wei
Available date2022-10-29T22:44:17Z
Publication Date2021-10-01
Publication NameIEEE Journal of Biomedical and Health Informatics
Identifierhttp://dx.doi.org/10.1109/JBHI.2021.3057627
CitationZhu, H., Han, G., Lin, C., Wang, M., Guizani, M., Hou, J., & Xing, W. (2021). Two-Way MR-Forest Based Growing Path Classification for Malignancy Estimation of Pulmonary Nodules. IEEE Journal of Biomedical and Health Informatics, 25(10), 3752-3762.‏
ISSN21682194
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101447497&origin=inward
URIhttp://hdl.handle.net/10576/35581
AbstractThis paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of L time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs' attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs' fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0.912, which exceeds those of MR-Forest and 3DDCNNs about 2.8% and 4.7%, respectively.
SponsorThis work was supported in part by the National Key Research, and Development Program under Grant 2018YFB1702003, in part by the National Science Foundation of China under Grant 61806048, and in part by the Open Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. Under Grant NRIHTOP1802.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCADs
counterfactual path elimination
Malignancy estimation
pseudo-spatiotemporal growing path generation
two-way cascade forest
TitleTwo-Way MR-Forest Based Growing Path Classification for Malignancy Estimation of Pulmonary Nodules
TypeArticle
Pagination3752-3762
Issue Number10
Volume Number25
dc.accessType Abstract Only


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