Výsledky vyhledávání - "Maayan Frid-Adar"

Automated Triage Of Covid-19 From Various Lung Abnormalities Using Chest Ct Features

Autor: Jannette Nassar, Dor Amran, Hayit Greenspan, Maayan Frid-Adar, Nimrod Sagie, Asher Kabakovitch

Publikováno v: ISBI

The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed8734e292b66d7d6ef7326a56e460bb
https://doi.org/10.1109/isbi48211.2021.9433803

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A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis

Autor: Maayan Frid-Adar, Rula Amer, Dor Amran, Asher Kabakovitch, Nimrod Sagie, Hayit Greenspan, Ophir Gozes

Publikováno v: Thoracic Image Analysis
Lecture Notes in Computer Science
Thoracic Image Analysis ISBN: 9783030624682
TIA@MICCAI

The outbreak of the COVID-19 global pandemic has affected millions and has a severe impact on our daily lives. To support radiologists in this overwhelming challenge, we developed a weakly supervised deep learning framework that can detect, localize,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2f79e356a23804003cb6cbdbaed7dfc

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COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

Autor: Rula Amer, Jannette Nassar, Ophir Gozes, Hayit Greenspan, Maayan Frid-Adar

In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in che

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db214e8eda61012748c6896bd76669ac

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Endotracheal Tube Detection and Segmentation in Chest Radiographs Using Synthetic Data

Autor: Maayan Frid-Adar, Hayit Greenspan, Rula Amer

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030322250
MICCAI (6)

Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET) tube is critical for the patient. In this study we propose a novel au

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9a3d802095fd557e3e7a66c11f11135a
https://doi.org/10.1007/978-3-030-32226-7_87

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GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

Autor: Maayan Frid-Adar, Michal Amitai, Jacob Goldberger, Hayit Greenspan, Idit Diamant, Eyal Klang

Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f547405f28231b1397d913a4c25e2635
http://arxiv.org/abs/1803.01229

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Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

Autor: Eyal Klang, Jacob Goldberger, Maayan Frid-Adar, Michal Amitai, Hayit Greenspan

Publikováno v: ISBI

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb51d559b0cfeccc5c0c5739a0f7b467
http://arxiv.org/abs/1801.02385

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Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

Autor: Avi Ben-Cohen, Maayan Frid-Adar, Rula Amer, Hayit Greenspan

Publikováno v: Image Analysis for Moving Organ, Breast, and Thoracic Images ISBN: 9783030009458
RAMBO+BIA+TIA@MICCAI

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::62f6e8dde84f2951f4e4a9d12f8e7444
https://doi.org/10.1007/978-3-030-00946-5_17

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Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN

Autor: Michal Amitai, Jacob Goldberger, Maayan Frid-Adar, Idit Diamant, Eyal Klang, Hayit Greenspan

Publikováno v: Patch-Based Techniques in Medical Imaging ISBN: 9783319674339
Patch-MI@MICCAI

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::51ce44574d3a731ad7138f5c77ed563b
https://doi.org/10.1007/978-3-319-67434-6_15

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