Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Gregg Belous"'
Autor:
Ashley G. Gillman, Febrio Lunardo, Joseph Prinable, Gregg Belous, Aaron Nicolson, Hang Min, Andrew Terhorst, Jason A. Dowling
Publikováno v:
Physical and Engineering Sciences in Medicine
Objectives: To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc1ec4e18eab59ca56a588272c56d1ac
http://europepmc.org/articles/PMC8678975
http://europepmc.org/articles/PMC8678975
Autor:
Joshua Peters, Leo Lebrat, Rodrigo Santa Cruz, Aaron Nicolson, Gregg Belous, Salamata Konate, Parnesh Raniga, Vincent Dore, Pierrick Bourgeat, Jurgen Mejan-Fripp, Clinton Fookes, Olivier Salvado
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cffd209f7d94cbb087560baaedb9826
https://doi.org/10.1109/wacv56688.2023.00200
https://doi.org/10.1109/wacv56688.2023.00200
Publikováno v:
Computer methods and programs in biomedicine. 200
Background and Objective: Shape prior models play a vital role for segmentation in medical image analysis. These models are most effective when shape variations can be captured by a parametric distribution, and sufficient training data is available.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60ead1b6b66affec0f521c58b191566a
https://pubmed.ncbi.nlm.nih.gov/33303290
https://pubmed.ncbi.nlm.nih.gov/33303290
Publikováno v:
Pattern Recognition. 111:107581
In this paper, we propose a dual subspace discriminative projection learning (DSDPL) framework for multi-category image classification. Our approach reflects the notion that images are composed of class-shared information, class-specific information,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f689fedc0b53c35ca89212b0e5d05a5e
https://doi.org/10.1016/j.patcog.2020.107581
https://doi.org/10.1016/j.patcog.2020.107581
Publikováno v:
DICTA
Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3293f394f15eb7b80cbbad400650f3f0
https://doi.org/10.1109/dicta.2018.8615773
https://doi.org/10.1109/dicta.2018.8615773
Publikováno v:
DICTA
Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c7937f273d077048d3ddaf28584442b
https://doi.org/10.1109/dicta.2016.7797080
https://doi.org/10.1109/dicta.2016.7797080
Publikováno v:
2013 1st International Conference on Artificial Intelligence, Modelling and Simulation.
This paper presents a model-based learning segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound (US) images by combining a random forest classifier with an active shape model (ASM). Our method applies an ASM
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::497ec8d7c295ee048b6e25de4e1f0154
https://doi.org/10.1109/aims.2013.58
https://doi.org/10.1109/aims.2013.58