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Paul F. Jäger
Researcher at German Cancer Research Center
Publications - 19
Citations - 1818
Paul F. Jäger is an academic researcher from German Cancer Research Center. The author has contributed to research in topics: Deep learning & Image segmentation. The author has an hindex of 8, co-authored 19 publications receiving 972 citations.
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Journal ArticleDOI
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard,Alain Lalande,Clement Zotti,Frederick Cervenansky,Xin Yang,Pheng-Ann Heng,Irem Cetin,Karim Lekadir,Oscar Camara,Miguel Ángel González Ballester,Gerard Sanroma,Sandy Napel,Steffen E. Petersen,Georgios Tziritas,Elias Grinias,Mahendra Khened,Varghese Alex Kollerathu,Ganapathy Krishnamurthi,Marc-Michel Rohé,Xavier Pennec,Maxime Sermesant,Fabian Isensee,Paul F. Jäger,Klaus H. Maier-Hein,Peter M. Full,Ivo Wolf,Sandy Engelhardt,Christian F. Baumgartner,Lisa M. Koch,Jelmer M. Wolterink,Ivana Išgum,Yeonggul Jang,Yoonmi Hong,Jay Patravali,Shubham Jain,Olivier Humbert,Pierre-Marc Jodoin +36 more
TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
Journal ArticleDOI
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
TL;DR: Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks, demonstrating a vast hidden potential in the systematic adaptation of deep learning methods to different datasets.
Posted Content
nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.
TL;DR: nU-Net ('no-new-Net'), a framework that automatically adapts itself to any given new dataset, is presented, which achieves state of the art performance on six well-established segmentation challenges.
Book ChapterDOI
nnU-Net for Brain Tumor Segmentation
TL;DR: The nnU-Net as mentioned in this paper achieved the first position in the BraTS 2020 challenge with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor.