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

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Publikationen

Suchbegriffe: DEEP LEARNING, . Treffer: 66

2023

Cognolato, F; O'Brien, K; Jin, J; Robinson, S; Laun, FB; Barth, M; Bollmann, S NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data
MED IMAGE ANAL. 2023; 84: 102700 Doi: 10.1016/j.media.2022.102700
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Evans, TE; Knol, MJ; Schwingenschuh, P; Wittfeld, K; Hilal, S; Ikram, MA; Dubost, F; van, Wijnen, KMH; Katschnig, P; Yilmaz, P; de, Bruijne, M; Habes, M; Chen, C; Langer, S; Völzke, H; Ikram, MK; Grabe, HJ; Schmidt, R; Adams, HHH; Vernooij, MW Determinants of Perivascular Spaces in the General Population: A Pooled Cohort Analysis of Individual Participant Data.
Neurology. 2023; 100(2):e107-e122 Doi: 10.1212/WNL.0000000000201349 [OPEN ACCESS]
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Holzinger, A; Keiblinger, K; Holub, P; Zatloukal, K; Müller, H AI for life: Trends in artificial intelligence for biotechnology.
N Biotechnol. 2023; 74:16-24 Doi: 10.1016/j.nbt.2023.02.001
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Jantscher, M; Gunzer, F; Kern, R; Hassler, E; Tschauner, S; Reishofer, G Information extraction from German radiological reports for general clinical text and language understanding.
Sci Rep. 2023; 13(1): 2353 Doi: 10.1038/s41598-023-29323-3 [OPEN ACCESS]
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Kommoss, KS; Winkler, JK; Mueller-Christmann, C; Bardehle, F; Toberer, F; Stolz, W; Kraenke, T; Hofmann-Wellenhof, R; Blum, A; Enk, A; Rosenberger, A; Haenssle, HA Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.
Eur J Cancer. 2023; 185:53-60 Doi: 10.1016/j.ejca.2023.02.025
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L'Imperio, V; Wulczyn, E; Plass, M; Müller, H; Tamini, N; Gianotti, L; Zucchini, N; Reihs, R; Corrado, GS; Webster, DR; Peng, LH; Chen, PC; Lavitrano, M; Liu, Y; Steiner, DF; Zatloukal, K; Pagni, F Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification.
JAMA Netw Open. 2023; 6(3):e2254891 Doi: 10.1001/jamanetworkopen.2022.54891 [OPEN ACCESS]
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Monaci, S; Qian, S; Gillette, K; Puyol-Antón, E; Mukherjee, R; Elliott, MK; Whitaker, J; Rajani, R; O'Neill, M; Rinaldi, CA; Plank, G; King, AP; Bishop, MJ Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices.
Europace. 2023; 25(2):469-477 Doi: 10.1093/europace/euac178 [OPEN ACCESS]
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Schulz, S; Abdulnazar, A; Kreuzthaler, M Clustering Similar Diagnosis Terms.
Stud Health Technol Inform. 2023; 302:837-838 Doi: 10.3233/SHTI230284
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2022

Egger, J; Gsaxner, C; Pepe, A; Pomykala, KL; Jonske, F; Kurz, M; Li, JN; Kleesiek, J Medical deep learning-A systematic meta-review
COMPUT METH PROG BIO. 2022; 221: 106874 Doi: 10.1016/j.cmpb.2022.106874
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Güllmar, D; Jacobsen, N; Deistung, A; Timmann, D; Ropele, S; Reichenbach, JR Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis.
Z Med Phys. 2022; 32(3): 346-360. Doi: 10.1016/j.zemedi.2021.11.004 [OPEN ACCESS]
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Herrera, CR; Grandits, T; Plank, G; Perdikaris, P; Costabal, FS; Pezzuto, S Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
ENG COMPUT-GERMANY. 2022; Doi: 10.1007/s00366-022-01709-3
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Kau, T; Ziurlys, M; Taschwer, M; Kloss-Brandstätter, A; Grabner, G; Deutschmann, H FDA-approved deep learning software application versus radiologists with different levels of expertise: detection of intracranial hemorrhage in a retrospective single-center study.
Neuroradiology. 2022; Doi: 10.1007/s00234-021-02874-w
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Kirnbauer, B; Hadzic, A; Jakse, N; Bischof, H; Stern, D Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.
J Endod. 2022; 48(11):1434-1440 Doi: 10.1016/j.joen.2022.07.013
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Lu, AX; Lu, AX; Pritišanac, I; Zarin, T; Forman-Kay, JD; Moses, AM Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.
PLoS Comput Biol. 2022; 18(6): e1010238 Doi: 10.1371/journal.pcbi.1010238 [OPEN ACCESS]
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Milling, M; Baird, A; Bartl-Pokorny, KD; Liu, S; Alcorn, AM; Shen, J; Tavassoli, T; Ainger, E; Pellicano, E; Pantic, M; Cummins, N; Schuller, BW Evaluating the Impact of Voice Activity Detection on Speech Emotion Recognition for Autistic Children
FRONT COMP SCI-SWITZ. 2022; 4: 837269 Doi: 10.3389/fcomp.2022.837269
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Milling, M; Pokorny, FB; Bartl-Pokorny, KD; Schuller, BW Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell.
Front Digit Health. 2022; 4: 886615 Doi: 10.3389/fdgth.2022.886615 [OPEN ACCESS]
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Müller, H; Holzinger, A; Plass, M; Brcic, L; Stumptner, C; Zatloukal, K Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European In Vitro Diagnostic Regulation.
N Biotechnol. 2022; 70:67-72 Doi: 10.1016/j.nbt.2022.05.002
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Pfeifer, B; Saranti, A; Holzinger, A GNN-SubNet: disease subnetwork detection with explainable graph neural networks.
Bioinformatics. 2022; 38(Supplement_2): ii120-ii126. Doi: 10.1093/bioinformatics/btac478
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Radl, L; Jin, Y; Pepe, A; Li, J; Gsaxner, C; Zhao, FH; Egger, J AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks.
Data Brief. 2022; 40:107801 Doi: 10.1016/j.dib.2022.107801 [OPEN ACCESS]
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Siegerist, F; Hay, E; Dikou, JS; Pollheimer, M; Büscher, A; Oh, J; Ribback, S; Zimmermann, U; Bräsen, JH; Lenoir, O; Drenic, V; Eller, K; Tharaux, PL; Endlich, N ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry.
J Cell Mol Med. 2022; 26(12): 3513-3526. Doi: 10.1111/jcmm.17392 [OPEN ACCESS]
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Sorantin, E; Grasser, MG; Hemmelmayr, A; Tschauner, S; Hrzic, F; Weiss, V; Lacekova, J; Holzinger, A The augmented radiologist: artificial intelligence in the practice of radiology.
Pediatr Radiol. 2022; 52(11):2074-2086 Doi: 10.1007/s00247-021-05177-7 [OPEN ACCESS]
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2021

Bilgic, B; Langkammer, C; Marques, JP; Meineke, J; Milovic, C; Schweser, F, , QSM, Challenge, 2.0, Organization, Committee QSM reconstruction challenge 2.0: Design and report of results.
Magn Reson Med. 2021; 86(3):1241-1255 Doi: 10.1002/mrm.28754 [OPEN ACCESS]
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Diouri, O; Cigler, M; Vettoretti, M; Mader, JK; Choudhary, P; Renard, E, , HYPO-RESOLVE, Consortium Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.
Diabetes Metab Res Rev. 2021; e3449 Doi: 10.1002/dmrr.3449 [OPEN ACCESS]
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Egger, J; Pepe, A; Gsaxner, C; Jin, Y; Li, JN; Kern, R Deep learning-a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact
PEERJ COMPUT SCI. 2021; 7: e773 Doi: 10.7717/peerj-cs.773
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Gamble, P; Jaroensri, R; Wang, H; Tan, F; Moran, M; Brown, T; Flament-Auvigne, I; Rakha, EA; Toss, M; Dabbs, DJ; Regitnig, P; Olson, N; Wren, JH; Robinson, C; Corrado, GS; Peng, LH; Liu, Y; Mermel, CH; Steiner, DF; Chen, PC Determining breast cancer biomarker status and associated morphological features using deep learning.
Commun Med (Lond). 2021; 1: 14 Doi: 10.1038/s43856-021-00013-3 [OPEN ACCESS]
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Haenssle, HA; Winkler, JK; Fink, C; Toberer, F; Enk, A; Stolz, W; Deinlein, T; Hofmann-Wellenhof, R; Kittler, H; Tschandl, P; Rosendahl, C; Lallas, A; Blum, A; Abassi, MS; Thomas, L; Tromme, I; Rosenberger, A; Reader study level-I and level-II Groups Christina Alt Skin lesions of face and scalp - Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.
Eur J Cancer. 2021; 144:192-199 Doi: 10.1016/j.ejca.2020.11.034
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Hepp, T; Blum, D; Armanious, K; Scholkopf, B; Stern, D; Yang, B; Gatidis, S Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study
COMPUT MED IMAG GRAP. 2021; 92: 101967 Doi: 10.1016/j.compmedimag.2021.101967 [OPEN ACCESS]
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Hrzic, F; Janisch, M; Stajduhar, I; Lerga, J; Sorantin, E; Tschauner, S Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs
MATHEMATICS-BASEL. 2021; 9(24): 3227 Doi: 10.3390/math9243227
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Lara, Hernandez, KA; Rienmüller, T; Baumgartner, D; Baumgartner, C Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability.
Comput Biol Med. 2021; 130:104200 Doi: 10.1016/j.compbiomed.2020.104200 [OPEN ACCESS]
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Li, J; Gsaxner, C; Pepe, A; Morais, A; Alves, V; von Campe, G; Wallner, J; Egger, J Synthetic skull bone defects for automatic patient-specific craniofacial implant design.
Sci Data. 2021; 8(1):36-36 Doi: 10.1038/s41597-021-00806-0 [OPEN ACCESS]
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Li, J; Krall, M; Trummer, F; Memon, AR; Pepe, A; Gsaxner, C; Jin, Y; Chen, X; Deutschmann, H; Zefferer, U; Schäfer, U; Campe, GV; Egger, J MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants.
Data Brief. 2021; 39: 107524 Doi: 10.1016/j.dib.2021.107524 [OPEN ACCESS]
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Li, J; Pimentel, P; Szengel, A; Ehlke, M; Lamecker, H; Zachow, S; Estacio, L; Doenitz, C; Ramm, H; Shi, H; Chen, X; Matzkin, F; Newcombe, V; Ferrante, E; Jin, Y; Ellis, DG; Aizenberg, MR; Kodym, O; Spanel, M; Herout, A; Mainprize, JG; Fishman, Z; Hardisty, MR; Bayat, A; Shit, S; Wang, B; Liu, Z; Eder, M; Pepe, A; Gsaxner, C; Alves, V; Zefferer, U; von, Campe, G; Pistracher, K; Schafer, U; Schmalstieg, D; Menze, BH; Glocker, B; Egger, J AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.
IEEE Trans Med Imaging. 2021; 40(9):2329-2342 Doi: 10.1109/TMI.2021.3077047
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Li, J; von, Campe, G; Pepe, A; Gsaxner, C; Wang, E; Chen, X; Zefferer, U; Tödtling, M; Krall, M; Deutschmann, H; Schäfer, U; Schmalstieg, D; Egger, J Automatic skull defect restoration and cranial implant generation for cranioplasty.
MED IMAGE ANAL. 2021; 73: 102171 Doi: 10.1016/j.media.2021.102171
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Majnarić, LT; Babič, F; O'Sullivan, S; Holzinger, A AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.
J CLIN MED. 2021; 10(4): 766 Doi: 10.3390/jcm10040766 [OPEN ACCESS]
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Memon, AR; Li, JN; Egger, J; Chen, XJ A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques
EXPERT REV MED DEVIC. 2021; Doi: 10.1080/17434440.2021.1969914
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Monaci, S; Gillette, K; Puyol-Antón, E; Rajani, R; Plank, G; King, A; Bishop, M Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach.
Front Physiol. 2021; 12:682446 Doi: 10.3389/fphys.2021.682446 [OPEN ACCESS]
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Ranjan, R; Partl, R; Erhart, R; Kurup, N; Schnidar, H The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.
Comput Biol Med. 2021; 139:104952 Doi: 10.1016/j.compbiomed.2021.104952
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Silva, N; Zhang, D; Kulvicius, T; Gail, A; Barreiros, C; Lindstaedt, S; Kraft, M; Bölte, S; Poustka, L; Nielsen-Saines, K; Wörgötter, F; Einspieler, C; Marschik, PB The future of General Movement Assessment: The role of computer vision and machine learning - A scoping review.
Res Dev Disabil. 2021; 110:103854 Doi: 10.1016/j.ridd.2021.103854 [OPEN ACCESS]
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Tschauner, S; Zellner, M; Pistorius, S; Gnannt, R; Schraner, T; Kellenberger, CJ Ultra-low-dose lung multidetector computed tomography in children - Approaching 0.2 millisievert.
Eur J Radiol. 2021; 139:109699 Doi: 10.1016/j.ejrad.2021.109699 [OPEN ACCESS]
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Wulczyn, E; Steiner, DF; Moran, M; Plass, M; Reihs, R; Tan, F; Flament-Auvigne, I; Brown, T; Regitnig, P; Chen, PC; Hegde, N; Sadhwani, A; MacDonald, R; Ayalew, B; Corrado, GS; Peng, LH; Tse, D; Müller, H; Xu, Z; Liu, Y; Stumpe, MC; Zatloukal, K; Mermel, CH Interpretable survival prediction for colorectal cancer using deep learning.
NPJ Digit Med. 2021; 4(1): 71-71. Doi: 10.1038/s41746-021-00427-2 [OPEN ACCESS]
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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; Doi: 10.1111/jdv.16165 [OPEN ACCESS]
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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 Doi: 10.1016/j.jtho.2020.01.025 [OPEN ACCESS]
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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. Doi: 10.1016/j.annonc.2019.10.013 [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. Doi: 10.1055/s-0040-1702001 [OPEN ACCESS]
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Holzinger, A; Langs, G; Denk, H; Zatloukal, K; Müller, H Causability and explainability of artificial intelligence in medicine.
Wiley Interdiscip Rev Data Min Knowl Discov. 2020; 9(4): e1312-e1312. Doi: 10.1002/widm.1312 [OPEN ACCESS]
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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
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Lopatina, A; Ropele, S; Sibgatulin, R; Reichenbach, JR; Güllmar, D Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis.
Front Neurosci. 2020; 14:609468-609468 Doi: 10.3389/fnins.2020.609468 [OPEN ACCESS]
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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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Messner, E; Fediuk, M; Swatek, P; Scheidl, S; Smolle-Jüttner, FM; Olschewski, H; Pernkopf, F Multi-channel lung sound classification with convolutional recurrent neural networks.
Comput Biol Med. 2020; 122(4):103831-103831 Doi: 10.1016/j.compbiomed.2020.103831
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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 Doi: 10.1016/j.ejca.2019.11.020
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