Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Publikationen
Suchbegriffe: DEEP LEARNING - , . Treffer: 7
2020
Liu, Y; Jain, A; Eng, C; Way, DH; Lee, K; Bui, P; Kanada, K; de Oliveira Marinho, G; Gallegos, J; Gabriele, S; Gupta, V; Singh, N; Natarajan, V; Hofmann-Wellenhof, R; Corrado, GS; Peng, LH; Webster, DR; Ai, D; Huang, SJ; Liu, Y; Dunn, RC; Coz, D
A deep learning system for differential diagnosis of skin diseases.
Nat Med. 2020; 26(6):900-908
Doi: 10.1038/s41591-020-0842-3
Web of Science
PubMed
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Marchetti, MA; Liopyris, K; Dusza, SW; Codella, NCF; Gutman, DA; Helba, B; Kalloo, A; Halpern, AC; Soyer, HP; Curiel-Lewandrowski, C; Kittler, H; Caffery, L; Malvehy, J; Wellenhof, RH
Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
J Am Acad Dermatol. 2020; 82(3):622-627
Doi: 10.1016/j.jaad.2019.07.016
[OPEN ACCESS]
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2019
Bloice, MD; Roth, PM; Holzinger, A
Biomedical image augmentation using Augmentor.
Bioinformatics. 2019; 35(21):4522-4524
Doi: 10.1093/bioinformatics/btz259
Web of Science
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Bollmann, S; Kristensen, MH; Larsen, MS; Olsen, MV; Pedersen, MJ; Østergaard, LR; O'Brien, K; Langkammer, C; Fazlollahi, A; Barth, M
SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network.
Z Med Phys. 2019; 29(2):139-149
Doi: 10.1016/j.zemedi.2019.01.001
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Bollmann, S; Rasmussen, KGB; Kristensen, M; Blendal, RG; Østergaard, LR; Plocharski, M; O'Brien, K; Langkammer, C; Janke, A; Barth, M
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.
Neuroimage. 2019; 195(2):373-383
Doi: 10.1016/j.neuroimage.2019.03.060
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Gsaxner, C; Roth, PM; Wallner, J; Egger, J
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data.
PLoS One. 2019; 14(3):e0212550-e0212550
Doi: 10.1371/journal.pone.0212550
[OPEN ACCESS]
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Lindner, L; Narnhofer, D; Weber, M; Gsaxner, C; Kolodziej, M; Egger, J
Using Synthetic Training Data for Deep Learning-Based GBM Segmentation.
Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019(4):6724-6729
Doi: 10.1109/EMBC.2019.8856297
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