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المؤلفZhu, Hongbo
المؤلفHan, Guangjie
المؤلفLin, Chuan
المؤلفWang, Min
المؤلفGuizani, Mohsen
المؤلفHou, Jianxia
المؤلفXing, Wei
تاريخ الإتاحة2022-10-29T22:44:17Z
تاريخ النشر2021-10-01
اسم المنشورIEEE Journal of Biomedical and Health Informatics
المعرّفhttp://dx.doi.org/10.1109/JBHI.2021.3057627
الاقتباسZhu, 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.‏
الرقم المعياري الدولي للكتاب21682194
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101447497&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/35581
الملخصThis 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.
راعي المشروعThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCADs
counterfactual path elimination
Malignancy estimation
pseudo-spatiotemporal growing path generation
two-way cascade forest
العنوانTwo-Way MR-Forest Based Growing Path Classification for Malignancy Estimation of Pulmonary Nodules
النوعArticle
الصفحات3752-3762
رقم العدد10
رقم المجلد25
dc.accessType Abstract Only


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