Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Nicholas Sachs"'
Publikováno v:
Academic Radiology. 29:S156-S164
Rationale and Objectives To train and validate machine learning models capable of classifying suspicious thoracic lesions as benign or malignant and to further classify malignant lesions by pathologic subtype while quantifying feature importance for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f5959647d61a9222c92668f25dbc57c
https://doi.org/10.1016/j.acra.2021.07.002
https://doi.org/10.1016/j.acra.2021.07.002
Publikováno v:
Academic Radiology. 28:608-618
Purpose CT guided transthoracic biopsy (CTTB) is an established, minimally invasive method for diagnostic evaluation of a variety of thoracic diseases. We assessed a large CTTB cohort diagnostic accuracy, complication rates, and developed machine lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a75a5a0af65232958553c425d67d7ae6
https://doi.org/10.1016/j.acra.2020.03.036
https://doi.org/10.1016/j.acra.2020.03.036
Publikováno v:
Image Analysis for Moving Organ, Breast, and Thoracic Images ISBN: 9783030009458
RAMBO+BIA+TIA@MICCAI
RAMBO+BIA+TIA@MICCAI
The goal of this study is to train and assess the performance of a deep 3D convolutional network (3D-CNN) in classifying indeterminate lung nodules as either benign or malignant based solely on diagnostic-grade thoracic CT imaging. While prior studie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25e1c0068eca88109fd7528f89c1034f
https://doi.org/10.1007/978-3-030-00946-5_29
https://doi.org/10.1007/978-3-030-00946-5_29
