Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Gregory Holste"'
Autor:
Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H. Van Tassel, Kyle Kovacs, Emily Y. Chew, Zhiyong Lu, Zhangyang Wang, Yifan Peng
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-13 (2024)
Abstract Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of ac
Externí odkaz:
https://doaj.org/article/d5bb7ebb0d8746ec9edb8eafe05c20f8
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-10 (2024)
Abstract Background Advances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for
Externí odkaz:
https://doaj.org/article/e689effff1514b029f5428764ad74f65
Autor:
Jonathan Burkow, Gregory Holste, Jeffrey Otjen, Francisco Perez, Joseph Junewick, Andy Zbojniewicz, Erin Romberg, Sarah Menashe, Jamie Frost, Adam Alessio
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by oth
Externí odkaz:
https://doaj.org/article/b751d94d05a14bd4b1c11f4bee38bbc0
Autor:
Mingquan Lin, Tianhao Li, Yifan Yang, Gregory Holste, Ying Ding, Sarah H. Van Tassel, Kyle Kovacs, George Shih, Zhangyang Wang, Zhiyong Lu, Fei Wang, Yifan Peng
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-9 (2023)
Abstract Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting in
Externí odkaz:
https://doaj.org/article/2303727d53f34ecabc28cb178ef5db30
Publikováno v:
IEEE transactions on medical imaging.
Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches of medical images. However, extracting discriminative radiomic features
Autor:
Gregory Holste, Evangelos K. Oikonomou, Bobak J. Mortazavi, Andreas Coppi, Kamil F. Faridi, Edward J. Miller, John K. Forrest, Robert L. McNamara, Lucila Ohno-Machado, Neal Yuan, Aakriti Gupta, David Ouyang, Harlan M. Krumholz, Zhangyang Wang, Rohan Khera
Background and AimsEarly diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6d7387b1fab9e2af4694422b8a4e468
https://doi.org/10.1101/2022.08.30.22279413
https://doi.org/10.1101/2022.08.30.22279413
Autor:
Jonathan Burkow, Gregory Holste, Jeffrey Otjen, Francisco Perez, Joseph Junewick, Adam Alessio
Publikováno v:
Medical Imaging 2022: Computer-Aided Diagnosis.
Autor:
Veer Sangha, Akshay Khunte, Gregory Holste, Bobak Mortazavi, Zhangyang Wang, Evangelos K. Oikonomou, Rohan Khera
Publikováno v:
Journal of the American College of Cardiology. 81:2403
Autor:
Gregory, Holste, Song, Wang, Ziyu, Jiang, Thomas C, Shen, George, Shih, Ronald M, Summers, Yifan, Peng, Zhangyang, Wang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031170263
Data Augment Label Imperfections (2022)
Data Augment Label Imperfections (2022)
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::754b53252140c9e1373cff5d26d0c56a
https://doi.org/10.1007/978-3-031-17027-0_3
https://doi.org/10.1007/978-3-031-17027-0_3
Publikováno v:
Medical Imaging: Image Processing
Chest radiographs are a common diagnostic tool in pediatric care, and several computer-augmented decision tasks for radiographs would benefit from knowledge of the anatomic locations within the thorax. For example, a pre-segmented chest radiograph co