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Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images.

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第一作者： Joana,Rocha

第一单位： Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal. joana866rocha@gmail.com.;INESC Technology and Science, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal. joana866rocha@gmail.com.

作者： Joana,Rocha ^[1] ; António,Cunha ^[2] ; Ana Maria,Mendonça ^[3]

作者单位： Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal. joana866rocha@gmail.com.;INESC Technology and Science, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal. joana866rocha@gmail.com. ^[1] INESC Technology and Science, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal.;Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal. ^[2] Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal.;INESC Technology and Science, Rua Dr. Roberto Frias s/n, 4200 - 465, Porto, Portugal. ^[3]

关键词 Computer-aided diagnosis Conventional Deep learning Image analysis Lung Nodule Segmentation

主题词诊断, 计算机辅助(Diagnosis, Computer-Assisted);人类(Humans);肺肿瘤(Lung Neoplasms);多发性肺结节(Multiple Pulmonary Nodules);放射摄影影像解释, 计算机辅助(Radiographic Image Interpretation, Computer-Assisted);体层摄影术, X线计算机(Tomography, X-Ray Computed)

DOI 10.1007/s10916-020-1541-9

PMID 32140870

发布时间 2021-01-22

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