A
Arkadiusz Gertych
Researcher at Cedars-Sinai Medical Center
Publications - 95
Citations - 3442
Arkadiusz Gertych is an academic researcher from Cedars-Sinai Medical Center. The author has contributed to research in topics: Medicine & Image processing. The author has an hindex of 24, co-authored 82 publications receiving 2414 citations. Previous affiliations of Arkadiusz Gertych include Silesian University of Technology & University of Southern California.
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Journal ArticleDOI
A deep convolutional neural network model to classify heartbeats
U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam,Arkadiusz Gertych,Ru San Tan +6 more
TL;DR: A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.
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Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction
TL;DR: Using a new digital hand atlas an image analysis methodology is being developed to assist radiologists in bone age estimation and describe the stage of skeletal development more objectively than visual comparison.
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Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Hamido Fujita,Shu Lih Oh,U. Raghavendra,Jen Hong Tan,Muhammad Adam,Arkadiusz Gertych,Yuki Hagiwara +9 more
TL;DR: A novel tool for an automated differentiation of shockable and non-shockable ventricular arrhythmias from 2 s electrocardiogram (ECG) segments is proposed and indicates that shockable life-threatening arrhythmia can be immediately detected and thus increase the chance of survival while CPR or AED-based support is performed.
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Bone age assessment of children using a digital hand atlas.
Arkadiusz Gertych,Aifeng Zhang,James W. Sayre,James W. Sayre,Sylwia Pospiech-Kurkowska,H. K. Huang +5 more
TL;DR: An automated method to assess bone age of children using a digital hand atlas and is being integrated with a PACS for validation of clinical use.
Journal ArticleDOI
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.
Arkadiusz Gertych,Zaneta Swiderska-Chadaj,Zhaoxuan Ma,Nathan Ing,Tomasz Markiewicz,Szczepan Cierniak,Hootan Salemi,Samuel Guzman,Ann E. Walts,Beatrice S. Knudsen +9 more
TL;DR: A pipeline equipped with a convolutional neural network and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas is constructed to assist with the quantification of growth patterns in lung adenocarcinomas.