Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Kayla Caughlin"'
Autor:
Kayla Caughlin, Elvis Duran-Sierra, Shuna Cheng, Rodrigo Cuenca, Beena Ahmed, Jim Ji, Mathias Martinez, Moustafa Al-Khalil, Hussain Al-Enazi, Yi-Shing Lisa Cheng, John Wright, Javier A. Jo, Carlos Busso
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 27:457-468
Deep learning approaches for medical image analysis are limited by small data set size due to multiple factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, pha
Publikováno v:
Proc SPIE Int Soc Opt Eng
Given the prevalence of cardiovascular diseases (CVDs), the segmentation of the heart on cardiac computed tomography (CT) remains of great importance. Manual segmentation is time-consuming and intra-and inter-observer variabilities yield inconsistent
Autor:
Kayla, Caughlin, Elvis, Duran-Sierra, Shuna, Cheng, Rodrigo, Cuenca, Beena, Ahmed, Jim, Ji, Vladislav V, Yakovlev, Mathias, Martinez, Moustafa, Al-Khalil, Hussain, Al-Enazi, Javier A, Jo, Carlos, Busso
Publikováno v:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis
Autor:
Jonathan Shoag, Christopher E. Barbieri, Kayla Caughlin, Baowei Fei, Daniel Margolis, Maysam Shahedi
Publikováno v:
Proc SPIE Int Soc Opt Eng
Accurate segmentation of the prostate on computed tomography (CT) has many diagnostic and therapeutic applications. However, manual segmentation is time-consuming and suffers from high inter- and intra-observer variability. Computer-assisted approach