S
Sven Koitka
Researcher at Dortmund University of Applied Sciences and Arts
Publications - 28
Citations - 843
Sven Koitka is an academic researcher from Dortmund University of Applied Sciences and Arts. The author has contributed to research in topics: Medicine & Metadata. The author has an hindex of 11, co-authored 20 publications receiving 369 citations. Previous affiliations of Sven Koitka include Technical University of Dortmund & New York University.
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Journal ArticleDOI
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter,Diana S. M. Buist,Christoph I. Lee,Yaroslav Nikulin,Dezső Ribli,Yuanfang Guan,William Lotter,Zequn Jie,Hao Du,Sijia Wang,Jiashi Feng,Mengling Feng,Hyo-Eun Kim,F. Albiol,Alberto Albiol,Stephen Morrell,Zbigniew Wojna,Mehmet Eren Ahsen,Umar Asif,Antonio Jimeno Yepes,Shivanthan A.C. Yohanandan,Simona Rabinovici-Cohen,Darvin Yi,Bruce Hoff,Thomas Yu,Elias Chaibub Neto,Daniel L. Rubin,Peter Lindholm,Laurie R. Margolies,Russell B. McBride,Joseph H. Rothstein,Weiva Sieh,Rami Ben-Ari,Stefan Harrer,Andrew D. Trister,Stephen H. Friend,Thea Norman,Berkman Sahiner,Fredrik Strand,Fredrik Strand,Justin Guinney,Gustavo Stolovitzky,Lester Mackey,Joyce Cahoon,Li Shen,Jae Ho Sohn,Hari Trivedi,Yiqiu Shen,Ljubomir Buturovic,Jose Costa Pereira,Jaime S. Cardoso,Eduardo Castro,Karl Trygve Kalleberg,Obioma Pelka,Imane Nedjar,Krzysztof J. Geras,Felix Nensa,Ethan Goan,Sven Koitka,Sven Koitka,Luis Caballero,David D. Cox,Pavitra Krishnaswamy,Gaurav Pandey,Christoph M. Friedrich,Dimitri Perrin,Clinton Fookes,Bibo Shi,Gerard Cardoso Negrie,Michael Kawczynski,Kyunghyun Cho,Can Son Khoo,Joseph Y. Lo,A. Gregory Sorensen,Hwejin Jung +74 more
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
Journal ArticleDOI
Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
TL;DR: In this article, a convolutional neural network based on different word embeddings was evaluated and compared to a classification based on user-level linguistic metadata, which achieved state-of-the-art results in a current early detection task.
Book ChapterDOI
Radiology Objects in COntext (ROCO): A Multimodal Image Dataset
Obioma Pelka,Obioma Pelka,Sven Koitka,Sven Koitka,Johannes Rückert,Felix Nensa,Christoph M. Friedrich +6 more
TL;DR: A new multimodal image dataset, with the aim of detecting the interplay between visual elements and semantic relations present in radiology images, is introduced by retrieving all image-caption pairs from the open-access biomedical literature database PubMedCentral.
Journal ArticleDOI
Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
TL;DR: In this paper, a convolutional neural network based on different word embeddings was evaluated and compared to a classification based on user-level linguistic metadata, which achieved state-of-the-art results in a current early detection task.
Word Embeddings and Linguistic Metadata at the CLEF 2018 Tasks for Early Detection of Depression and Anorexia.
TL;DR: FHDO Biomedical Computer Science Group (BCSG) has submitted results obtained from four machine learning models as well as from a final late fusion ensemble based on user-level linguistic metadata, Bags of Words, neural word embeddings, and Convolutional Neural Networks.