Literature DB >> 29708147

Deep learning and medical imaging.

Eyal Klang1.   

Abstract

Year:  2018        PMID： 29708147      PMCID： PMC5906243          DOI： 10.21037/jtd.2018.02.76

Source DB:  PubMed          Journal:  J Thorac Dis        ISSN： 2072-1439            Impact factor:   2.895


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  18 in total

1.  Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

Authors:  Francesco Ciompi; Bartjan de Hoop; Sarah J van Riel; Kaman Chung; Ernst Th Scholten; Matthijs Oudkerk; Pim A de Jong; Mathias Prokop; Bram van Ginneken
Journal:  Med Image Anal       Date:  2015-09-08       Impact factor: 8.545

Review 2.  Implementing Machine Learning in Radiology Practice and Research.

Authors:  Marc Kohli; Luciano M Prevedello; Ross W Filice; J Raymond Geis
Journal:  AJR Am J Roentgenol       Date:  2017-01-26       Impact factor: 3.959

3.  Lung nodule classification using deep feature fusion in chest radiography.

Authors:  Changmiao Wang; Ahmed Elazab; Jianhuang Wu; Qingmao Hu
Journal:  Comput Med Imaging Graph       Date:  2016-11-12       Impact factor: 4.790

4.  Pulmonary nodule classification with deep residual networks.

Authors:  Aiden Nibali; Zhen He; Dennis Wollersheim
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-05-13       Impact factor: 2.924

5.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.

Authors:  Marios Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou
Journal:  IEEE Trans Med Imaging       Date:  2016-02-29       Impact factor: 10.048

6.  A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

Authors:  Huafeng Wang; Tingting Zhao; Lihong Connie Li; Haixia Pan; Wanquan Liu; Haoqi Gao; Fangfang Han; Yuehai Wang; Yifan Qi; Zhengrong Liang
Journal:  J Xray Sci Technol       Date:  2018       Impact factor: 1.535

7.  2016 New Horizons Lecture: Beyond Imaging-Radiology of Tomorrow.

Authors:  Hedvig Hricak
Journal:  Radiology       Date:  2018-01-18       Impact factor: 11.105

Review 8.  When Machines Think: Radiology's Next Frontier.

Authors:  Keith J Dreyer; J Raymond Geis
Journal:  Radiology       Date:  2017-12       Impact factor: 11.105

9.  Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Authors:  QingZeng Song; Lei Zhao; XingKe Luo; XueChen Dou
Journal:  J Healthc Eng       Date:  2017-08-09       Impact factor: 2.682

10.  Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

Authors:  Kai-Lung Hua; Che-Hao Hsu; Shintami Chusnul Hidayati; Wen-Huang Cheng; Yu-Jen Chen
Journal:  Onco Targets Ther       Date:  2015-08-04       Impact factor: 4.147
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  16 in total

1.  Investigation of clinical target volume segmentation for whole breast irradiation using three-dimensional convolutional neural networks with gradient-weighted class activation mapping.

Authors:  Megumi Oya; Satoru Sugimoto; Keisuke Sasai; Kazuhito Yokoyama
Journal:  Radiol Phys Technol       Date:  2021-06-16

2.  Automated quantitative assessment of oncological disease progression using deep learning.

Authors:  Yiftach Barash; Eyal Klang
Journal:  Ann Transl Med       Date:  2019-12

3.  Promoting head CT exams in the emergency department triage using a machine learning model.

Authors:  Eyal Klang; Yiftach Barash; Shelly Soffer; Sigalit Bechler; Yehezkel S Resheff; Talia Granot; Moni Shahar; Maximiliano Klug; Gennadiy Guralnik; Eyal Zimlichman; Eli Konen
Journal:  Neuroradiology       Date:  2019-10-10       Impact factor: 2.804

4.  Automatic Diagnosis Labeling of Cardiovascular MRI by Using Semisupervised Natural Language Processing of Text Reports.

Authors:  Sameer Zaman; Camille Petri; Kavitha Vimalesvaran; James Howard; Anil Bharath; Darrel Francis; Nicholas Peters; Graham D Cole; Nick Linton
Journal:  Radiol Artif Intell       Date:  2021-11-24

5.  Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging.

Authors:  Yeon-Hee Lee; Yung-Kyun Noh; Jong Hyun Won; Seunghyeon Kim; Q-Schick Auh
Journal:  Sci Rep       Date:  2022-07-05       Impact factor: 4.996

Review 6.  Reinventing polysomnography in the age of precision medicine.

Authors:  Diane C Lim; Diego R Mazzotti; Kate Sutherland; Jesse W Mindel; Jinyoung Kim; Peter A Cistulli; Ulysses J Magalang; Allan I Pack; Philip de Chazal; Thomas Penzel
Journal:  Sleep Med Rev       Date:  2020-03-20       Impact factor: 11.609

7.  Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.

Authors:  Eyal Klang; Yiftach Barash; Asaf Levartovsky; Noam Barkin Lederer; Adi Lahat
Journal:  Clin Exp Gastroenterol       Date:  2021-05-05

8.  Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Authors:  Quincy A Hathaway; Skyler M Roth; Mark V Pinti; Daniel C Sprando; Amina Kunovac; Andrya J Durr; Chris C Cook; Garrett K Fink; Tristen B Cheuvront; Jasmine H Grossman; Ghadah A Aljahli; Andrew D Taylor; Andrew P Giromini; Jessica L Allen; John M Hollander
Journal:  Cardiovasc Diabetol       Date:  2019-06-11       Impact factor: 9.951

9.  Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Authors:  Harsh Panwar; P K Gupta; Mohammad Khubeb Siddiqui; Ruben Morales-Menendez; Vaishnavi Singh
Journal:  Chaos Solitons Fractals       Date:  2020-05-28       Impact factor: 5.944

10.  Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.

Authors:  Shelly Soffer; Eyal Klang; Orit Shimon; Yiftach Barash; Noa Cahan; Hayit Greenspana; Eli Konen
Journal:  Sci Rep       Date:  2021-08-04       Impact factor: 4.379
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