Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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Publikationen

Suchbegriffe: DEEP LEARNING, . Treffer: 19

2020

Fink, C; Blum, A; Buhl, T; Mitteldorf, C; Hofmann-Wellenhof, R; Deinlein, T; Stolz, W; Trennheuser, L; Cussigh, C; Deltgen, D; Winkler, JK; Toberer, F; Enk, A; Rosenberger, A; Haenssle, HA Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined nevi and melanomas.
J Eur Acad Dermatol Venereol. 2019;
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Galateau Salle, F; Le Stang, N; Tirode, F; Courtiol, P; Nicholson, AG; Tsao, MS; Tazelaar, HD; Churg, A; Dacic, S; Roggli, V; Pissaloux, D; Maussion, C; Moarii, M; Beasley, MB; Begueret, H; Chapel, DB; Copin, MC; Gibbs, AR; Klebe, S; Lantuejoul, S; Nabeshima, K; Vignaud, JM; Attanoos, R; Brcic, L; Capron, F; Chirieac, LR; Damiola, F; Sequeiros, R; Cazes, A; Damotte, D; Foulet, A; Giusiano-Courcambeck, S; Hiroshima, K; Hofman, V; Husain, AN; Kerr, K; Marchevsky, A; Paindavoine, S; Picquenot, JM; Rouquette, I; Sagan, C; Sauter, J; Thivolet, F; Brevet, M; Rouvier, P; Travis, WD; Planchard, G; Weynand, B; Clozel, T; Wainrib, G; Fernandez-Cuesta, L; Pairon, JC; Rusch, V; Girard, N Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.
J Thorac Oncol. 2020; 15(6):1037-1053
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Haenssle, HA; Fink, C; Toberer, F; Winkler, J; Stolz, W; Deinlein, T; Hofmann-Wellenhof, R; Lallas, A; Emmert, S; Buhl, T; Zutt, M; Blum, A; Abassi, MS; Thomas, L; Tromme, I; Tschandl, P; Enk, A; Rosenberger, A; Reader Study Level I and Level II Groups Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.
Ann Oncol. 2020; 31(1): 137-143. [OPEN ACCESS]
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Hahn, U; Oleynik, M Medical Information Extraction in the Age of Deep Learning.
Yearb Med Inform. 2020; 29(1): 208-220.
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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. [OPEN ACCESS]
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Messner, E; Fediuk, M; Swatek, P; Scheidl, S; Smolle-Juttner, FM; Olschewski, H; Pernkopf, F Multi-channel lung sound classification with convolutional recurrent neural networks
COMPUT BIOL MED. 2020; 122: 103831
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Winkler, JK; Sies, K; Fink, C; Toberer, F; Enk, A; Deinlein, T; Hofmann-Wellenhof, R; Thomas, L; Lallas, A; Blum, A; Stolz, W; Abassi, MS; Fuchs, T; Rosenberger, A; Haenssle, HA Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.
Eur J Cancer. 2020; 127: 21-29.
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2019

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
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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
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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. [OPEN ACCESS]
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Oleynik, M; Kugic, A; Kasáč, Z; Kreuzthaler, M Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.
J Am Med Inform Assoc. 2019; 26(11):1247-1254 [OPEN ACCESS]
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O'Sullivan, S; Ali, Z; Jiang, X; Abdolvand, R; Ünlü, MS; Silva, HPD; Baca, JT; Kim, B; Scott, S; Sajid, MI; Moradian, S; Mansoorzare, H; Holzinger, A Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing.
SENSORS-BASEL. 2019; 19(8): [OPEN ACCESS]
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Schirmer, MD; Dalca, AV; Sridharan, R; Giese, AK; Donahue, KL; Nardin, MJ; Mocking, SJT; McIntosh, EC; Frid, P; Wasselius, J; Cole, JW; Holmegaard, L; Jern, C; Jimenez-Conde, J; Lemmens, R; Lindgren, AG; Meschia, JF; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Thijs, V; Woo, D; Vagal, A; Xu, H; Kittner, SJ; McArdle, PF; Mitchell, BD; Rosand, J; Worrall, BB; Wu, O; Golland, P; Rost, NS; MRI-GENIE Investigators White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.
Neuroimage Clin. 2019; 23(9): 101884-101884. [OPEN ACCESS]
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Winkler, JK; Fink, C; Toberer, F; Enk, A; Deinlein, T; Hofmann-Wellenhof, R; Thomas, L; Lallas, A; Blum, A; Stolz, W; Haenssle, HA Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
JAMA Dermatol. 2019; [OPEN ACCESS]
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Wu, O; Winzeck, S; Giese, AK; Hancock, BL; Etherton, MR; Bouts, MJRJ; Donahue, K; Schirmer, MD; Irie, RE; Mocking, SJT; McIntosh, EC; Bezerra, R; Kamnitsas, K; Frid, P; Wasselius, J; Cole, JW; Xu, H; Holmegaard, L; Jiménez-Conde, J; Lemmens, R; Lorentzen, E; McArdle, PF; Meschia, JF; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Stanne, TM; Thijs, V; Vagal, A; Woo, D; Bevan, S; Kittner, SJ; Mitchell, BD; Rosand, J; Worrall, BB; Jern, C; Lindgren, AG; Maguire, J; Rost, NS Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.
Stroke. 2019; 50(7): 1734-1741. [OPEN ACCESS]
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Zhuang, X; Li, L; Payer, C; Štern, D; Urschler, M; Heinrich, MP; Oster, J; Wang, C; Smedby, Ö; Bian, C; Yang, X; Heng, PA; Mortazi, A; Bagci, U; Yang, G; Sun, C; Galisot, G; Ramel, JY; Brouard, T; Tong, Q; Si, W; Liao, X; Zeng, G; Shi, Z; Zheng, G; Wang, C; MacGillivray, T; Newby, D; Rhode, K; Ourselin, S; Mohiaddin, R; Keegan, J; Firmin, D; Yang, G Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.
Med Image Anal. 2019; 58:101537-101537 [OPEN ACCESS]
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2018

Egger, J; Pfarrkirchner, B; Gsaxner, C; Lindner, L; Schmalstieg, D; Wallner, J Fully Convolutional Mandible Segmentation on a valid Ground- Truth Dataset.
Conf Proc IEEE Eng Med Biol Soc. 2018; 2018: 656-660. [Oral Communication]
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2017

Kainz, P; Pfeiffer, M; Urschler, M Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.
PeerJ. 2017; 5(6):e3874-e3874 [OPEN ACCESS]
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2014

Minarro-Giménez, JA; Marín-Alonso, O; Samwald, M Exploring the application of deep learning techniques on medical text corpora.
Stud Health Technol Inform. 2014; 205(12): 584-588.
PubMed

 

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