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Erdi Calli
Researcher at Radboud University Nijmegen
Publications - 11
Citations - 322
Erdi Calli is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Computer science & Receiver operating characteristic. The author has an hindex of 6, co-authored 9 publications receiving 133 citations. Previous affiliations of Erdi Calli include Analysis Group.
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Journal ArticleDOI
Deep learning for chest X-ray analysis: A survey.
TL;DR: In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.
Journal ArticleDOI
Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence
Nikolas Lessmann,Clara I. Sánchez,Ludo F. M. Beenen,Luuk H. Boulogne,Monique Brink,Erdi Calli,Jean-Paul Charbonnier,Ton Dofferhoff,Wouter M. van Everdingen,Paul K. Gerke,Bram Geurts,Hester A. Gietema,Miriam Groeneveld,Louis D. van Harten,Nils Hendrix,Ward Hendrix,Henkjan J. Huisman,Ivana Išgum,Colin Jacobs,Ruben Kluge,Michel Kok,Jasenko Krdzalic,Bianca Lassen-Schmidt,Kicky G. van Leeuwen,James A. Meakin,Mike Overkamp,Tjalco van Rees Vellinga,Eva M. van Rikxoort,Riccardo Samperna,Cornelia M. Schaefer-Prokop,Steven Schalekamp,Ernst T. Scholten,Cheryl Sital,J. Lauran Stöger,Jonas Teuwen,Kiran Vaidhya Venkadesh,Coen de Vente,Marieke Vermaat,Weiyi Xie,Bram de Wilde,Mathias Prokop,Bram van Ginneken +41 more
TL;DR: In this paper, the authors developed and validated an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID19 Reporting and Data System (CO-RADS) and CT severity scoring systems.
Journal ArticleDOI
Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification
Ecem Sogancioglu,Keelin Murphy,Erdi Calli,Ernst T. Scholten,Steven Schalekamp,Bram van Ginneken +5 more
TL;DR: It is concluded that the segmentation-based model requires 100 times fewer annotated chest radiographs to achieve a substantially better performance, while also producing more interpretable results.
Journal Article
Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence.
Nikolas Lessmann,Clara I. Sánchez,Ludo F. M. Beenen,Luuk H. Boulogne,Monique Brink,Erdi Calli,Jean-Paul Charbonnier,Ton Dofferhoff,Wouter M. van Everdingen,Paul K. Gerke,Bram Geurts,Hester A. Gietema,Miriam Groeneveld,Louis D. van Harten,Nils Hendrix,Ward Hendrix,Henkjan J. Huisman,Ivana Išgum,Colin Jacobs,Ruben Kluge,Michel Kok,Jasenko Krdzalic,Bianca Lassen-Schmidt,Kicky G. van Leeuwen,James A. Meakin,Mike Overkamp,Tjalco van Rees Vellinga,Eva M. van Rikxoort,Riccardo Samperna,Cornelia M. Schaefer-Prokop,Steven Schalekamp,Ernst T. Scholten,Cheryl Sital,Lauran Stöger,Jonas Teuwen,Kiran Vaidhya Venkadesh,Coen de Vente,Marieke Vermaat,Weiyi Xie,Bram De Wilde,Mathias Prokop,Bram van Ginneken +41 more
TL;DR: In this article, the authors developed and validated an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the CO-RADS and CT severity scoring systems.
Proceedings ArticleDOI
Handling label noise through model confidence and uncertainty: application to chest radiograph classification
TL;DR: These situs inversus experiments confirm results from the computer vision literature that deep learning architectures are relatively robust but not completely insensitive to label noise in the training data: without or with very low noise, classification results are near perfect.