Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Seth Billings"'
Publikováno v:
PLoS ONE, Vol 12, Iss 8, p e0184059 (2017)
To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis
Externí odkaz:
https://doaj.org/article/2dd673b005914d97a648d3851723ca78
Autor:
Breanne Christie, Roksana Sadeghi, Arathy Kartha, Chigozie Ewulum, Avi Caspi, Francesco V. Tenore, Roberta L. Klatzky, Gislin Dagnelie, Seth Billings
Publikováno v:
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER).
Autor:
Breanne Christie, Roksana Sadeghi, Arathy Kartha, Avi Caspi, Francesco V. Tenore, Roberta L. Klatzky, Gislin Dagnelie, Seth Billings
Publikováno v:
J Neural Eng
ObjectiveElectrical stimulation of the retina can elicit flashes of light called phosphenes, which can be used to restore rudimentary vision for people with blindness. Functional sight requires stimulation of multiple electrodes to create patterned v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a49af226c8ead606e780863b4c76ca9
https://doi.org/10.1101/2022.03.08.22270800
https://doi.org/10.1101/2022.03.08.22270800
Autor:
Masaru Ishii, Russell H. Taylor, Gregory D. Hager, Seth Billings, Austin Reiter, Ayushi Sinha, Xingtong Liu
Publikováno v:
Med Image Anal
In this paper, we present three deformable registration algorithms designed within a paradigm that uses 3D statistical shape models to accomplish two tasks simultaneously: 1) register point features from previously unseen data to a statistically deri
Publikováno v:
ISBI
This study investigates unsupervised novelty detection (ND) for screening of rare myopathies and specifically myositis. To support this study we developed from the ground up a novel and fully annotated dataset consisting of 3586 images taken of eight
Publikováno v:
Computers in biology and medicine. 105
Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objectiv
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030012007
OR 2.0/CARE/CLIP/ISIC@MICCAI
OR 2.0/CARE/CLIP/ISIC@MICCAI
This study develops approaches for the automated referral of individuals with Lyme disease using erythema migrans rash (EM) images with clinical-grade or ‘in the wild’ characteristics. We develop a pre-screener using a Deep Convolutional Neural N
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86a1a080fac7952b991ee2085ecac8d4
https://doi.org/10.1007/978-3-030-01201-4_26
https://doi.org/10.1007/978-3-030-01201-4_26
Publikováno v:
ICMLA
This study addresses the development of machine learning methods for reduced space ultrasound to perform automated prescreening of breast cancer. The use of ultrasound in low-resource settings is constrained by lack of trained personnel and equipment
Autor:
Seth Billings, Kevin C. Wolfe, Kapil D. Katyal, Derek M. Rollend, Philippe Burlina, Paul E. Rosendall
Publikováno v:
Computer Vision – ACCV 2016 Workshops ISBN: 9783319544069
ACCV Workshops (1)
ACCV Workshops (1)
We describe the recent development of assistive computer vision algorithms for use with the Argus II retinal prosthesis system. While users of the prosthetic system can learn and adapt to the limited stimulation resolution, there exists great potenti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e7ada6d7b9a9e5f467239f6f7076de3
https://doi.org/10.1007/978-3-319-54407-6_20
https://doi.org/10.1007/978-3-319-54407-6_20
Autor:
Seth Billings, Masaru Ishii, Ayushi Sinha, Austin Reiter, Russell H. Taylor, Gregory D. Hager, Simon Leonard
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319467252
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ab36a949d846a1c4e80c2ce45dcb173
https://doi.org/10.1007/978-3-319-46726-9_73
https://doi.org/10.1007/978-3-319-46726-9_73