PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Healthcare Informatics Research10.4258/hir.2020.26.1.61202026161Web-Based Spine Segmentation Using Deep Learning in Computed Tomography ImagesYoung Jae Kim, Bilegt Ganbold, Kwang Gi Kimhttps://synapse.koreamed.org/pdf/10.4258/hir.2020.26.1.61, https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2020.26.1.61, https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2020.26.1.61
10.21203/rs.3.rs-65573/v12020Deep Learning Based Liver Cancer Segmentation from Computed Tomography ImagesYodit Abebe Ayalew, Kinde Anlay Fante, Mohammed Aliyhttps://www.researchsquare.com/article/rs-65573/v1, https://www.researchsquare.com/article/rs-65573/v1.html
10.21203/rs.3.rs-487824/v12021A Deep Learning Method for Estimating Patient Body Weight Using Computed Tomography Scout ImagesShota Ichikawa, Misaki Hamada, Hiroyuki Sugimorihttps://www.researchsquare.com/article/rs-487824/v1, https://www.researchsquare.com/article/rs-487824/v1.html
Journal of Cardiovascular Computed Tomography10.1016/j.jcct.2020.06.0172020143S20-S21Automated Multi-Chamber Segmentation And Imaging Plane Re-slicing Of Cardiac CT Images Via Deep LearningZ. Chen, M. Rigolli, D. Vigneault, A. Craine, F. Contijochhttps://api.elsevier.com/content/article/PII:S1934592520301908?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1934592520301908?httpAccept=text/plain
MATEC Web of Conferences10.1051/matecconf/201823202001201823202001A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography ImagesLi Zheng, Yiran Leihttps://www.matec-conferences.org/10.1051/matecconf/201823202001/pdf
Computed Tomography - Advanced Applications10.5772/675942017Novelty Detection‐Based Internal Fingerprint Segmentation in Optical Coherence Tomography ImagesRethabile Khutlang, Pheeha Machaka, Ann Singh, Fulufhelo Nelwamondohttp://www.intechopen.com/download/pdf/54257
Concurrent Engineering10.1177/1063293x21102143520211063293X2110214Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography imagesRT Subhalakshmi, S Appavu alias Balamurugan, S Sasikalahttp://journals.sagepub.com/doi/pdf/10.1177/1063293X211021435, http://journals.sagepub.com/doi/full-xml/10.1177/1063293X211021435, http://journals.sagepub.com/doi/pdf/10.1177/1063293X211021435
10.21203/rs.3.rs-82263/v22020Integration of Deep and Ensemble Learning for Detecting COVID-19 in Computed Tomography ImagesAli Haidar, Lois Hollowayhttps://www.researchsquare.com/article/rs-82263/v2, https://www.researchsquare.com/article/rs-82263/v2.html
10.21203/rs.3.rs-82263/v12020Integration of Deep and Ensemble Learning for Detecting COVID-19 in Computed Tomography ImagesAli Haidar, Lois Hollowayhttps://www.researchsquare.com/article/rs-82263/v1, https://www.researchsquare.com/article/rs-82263/v1.html
Journal of Healthcare Engineering10.1155/2021/4603475202120211-7Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion DetectionXueling Wang, Xianmin Meng, Shu Yanhttp://downloads.hindawi.com/journals/jhe/2021/4603475.pdf, http://downloads.hindawi.com/journals/jhe/2021/4603475.xml, http://downloads.hindawi.com/journals/jhe/2021/4603475.pdf