K
Krzysztof J. Geras
Researcher at New York University
Publications - 76
Citations - 3286
Krzysztof J. Geras is an academic researcher from New York University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 21, co-authored 65 publications receiving 1788 citations. Previous affiliations of Krzysztof J. Geras include Microsoft & University of Edinburgh.
Papers
More filters
Posted Content
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.
Jure Zbontar,Florian Knoll,Anuroop Sriram,Matthew J. Muckley,Mary Bruno,Aaron Defazio,Marc Parente,Krzysztof J. Geras,Joe Katsnelson,Hersh Chandarana,Zizhao Zhang,Michal Drozdzal,Adriana Romero,Michael G. Rabbat,Pascal Vincent,James Pinkerton,Duo Wang,Nafissa Yakubova,Erich James Owens,C. Lawrence Zitnick,Michael P. Recht,Daniel K. Sodickson,Yvonne W. Lui +22 more
TL;DR: The fastMRI dataset is introduced, a large-scale collection of both raw MR measurements and clinical MR images that can be used for training and evaluation of machine-learning approaches to MR image reconstruction.
Journal ArticleDOI
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Nan Wu,Jason Phang,Jungkyu Park,Yiqiu Shen,Zhe Huang,Masha Zorin,Stanisław Jastrzębski,Thibault Févry,Joe Katsnelson,Eric Kim,Stacey Wolfson,Ujas Parikh,Sushma Gaddam,Leng Leng Young Lin,Kara Ho,Joshua D. Weinstein,Beatriu Reig,Yiming Gao,Hildegard B. Toth,Kristine Pysarenko,Alana A. Lewin,Jiyon Lee,Krystal Airola,Eralda Mema,Stephanie H Chung,Esther Hwang,Naziya Samreen,S. Gene Kim,Laura Heacock,Linda Moy,Kyunghyun Cho,Krzysztof J. Geras +31 more
TL;DR: In this paper, a two-stage architecture and training procedure was proposed for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 100,000 images).
Posted Content
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Nan Wu,Jason Phang,Jungkyu Park,Yiqiu Shen,Zhe Huang,Masha Zorin,Stanisław Jastrzębski,Thibault Févry,Joe Katsnelson,Eric Kim,Stacey Wolfson,Ujas Parikh,Sushma Gaddam,Leng Leng Young Lin,Kara Ho,Joshua D. Weinstein,Beatriu Reig,Yiming Gao,Hildegard B. Toth,Kristine Pysarenko,Alana A. Lewin,Jiyon Lee,Krystal Airola,Eralda Mema,Stephanie H Chung,Esther Hwang,Naziya Samreen,S. Gene Kim,Laura Heacock,Linda Moy,Kyunghyun Cho,Krzysztof J. Geras +31 more
TL;DR: A deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams, and it is shown that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of the neural network is more accurate than either of the two separately.
Journal ArticleDOI
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.
Florian Knoll,Jure Zbontar,Anuroop Sriram,Matthew J. Muckley,Mary Bruno,Aaron Defazio,Marc Parente,Krzysztof J. Geras,Joe Katsnelson,Hersh Chandarana,Zizhao Zhang,Michal Drozdzalv,Adriana Romero,Michael G. Rabbat,Pascal Vincent,James Pinkerton,Duo Wang,Nafissa Yakubova,Erich James Owens,C. Lawrence Zitnick,Michael P. Recht,Daniel K. Sodickson,Yvonne W. Lui +22 more
TL;DR: A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
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.