A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation

PURPOSE: To evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation. MATERIALS AND METHODS: This retrospective, single-instituti...

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Publicado en:Radiol Artif Intell
Autores principales: Dreizin, David, Zhou, Yuyin, Fu, Shuhao, Wang, Yan, Li, Guang, Champ, Kathryn, Siegel, Eliot, Wang, Ze, Chen, Tina, Yuille, Alan L.
Formato: Artigo
Lenguaje:Inglês
Publicado: Radiological Society of North America 2020
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Original Research
Acceso en línea:https://ncbi.nlm.nih.gov/pmc/articles/PMC7706875/
https://ncbi.nlm.nih.gov/pubmed/33330848
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2020190220
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