Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Christian Huemmer"'
Autor:
Wai Yan Ryana Fok, Andreas Fieselmann, Christian Huemmer, Ramyar Biniazan, Marcel Beister, Bernhard Geiger, Steffen Kappler, Sylvia Saalfeld
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an
Externí odkaz:
https://doaj.org/article/0eca2ff17a324248aeab41fb4faa606e
Autor:
Jens Ricke, Michael Ingrisch, Balthasar Maria Schachtner, Andreas Fieselmann, Bastian O. Sabel, Awais Mansoor, Florin C. Ghesu, Philipp Wesp, Lena Trappmann, Christian Huemmer, Basel Munawwar, Johannes Rueckel
Publikováno v:
European Radiology
Objectives Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c58f722abf420df5b078cf789b581e0
https://doi.org/10.1007/s00330-021-07833-w
https://doi.org/10.1007/s00330-021-07833-w
Publikováno v:
IEEE Signal Processing Letters. 26:1827-1831
In this letter, we introduce a novel approach for nonlinear acoustic echo cancellation. The proposed approach uses the principle of transfer learning to train a neural network that approximates the nonlinear function responsible for the nonlinear dis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb131604063a54e8206338464de00525
https://doi.org/10.1109/lsp.2019.2951311
https://doi.org/10.1109/lsp.2019.2951311
Autor:
Fatemeh Homayounieh, Subba Digumarthy, Shadi Ebrahimian, Johannes Rueckel, Boj Friedrich Hoppe, Bastian Oliver Sabel, Sailesh Conjeti, Karsten Ridder, Markus Sistermanns, Lei Wang, Alexander Preuhs, Florin Ghesu, Awais Mansoor, Mateen Moghbel, Ariel Botwin, Ramandeep Singh, Samuel Cartmell, John Patti, Christian Huemmer, Andreas Fieselmann, Clemens Joerger, Negar Mirshahzadeh, Victorine Muse, Mannudeep Kalra
Publikováno v:
JAMA Network Open
Key Points Question Can artificial intelligence (AI) improve detection of pulmonary nodules on chest radiographs at different levels of detection difficulty? Findings In this diagnostic study, AI-aided interpretation was associated with significantly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89595635ddf6c5207ecbc7a01a0132de
http://europepmc.org/articles/PMC8717119
http://europepmc.org/articles/PMC8717119
Autor:
Jan Rudolph, Sophia Goller, Nicola Fink, Johannes Rueckel, Alexander Preuhs, Julien Dinkel, Florin-Cristian Ghesu, Jens Ricke, Michael Ingrisch, Najib Ben Khaled, Abishek Balachandran, Christian Huemmer, Bastian O. Sabel, Maximilian Fischer, Awais Mansoor, Vincent Schwarze, Andreas Fieselmann, Maximilian Jörgens, Vanessa Koliogiannis, Reddappagari Suryanarayana Vishwanath
Publikováno v:
Investigative radiology. 57(2)
OBJECTIVES Chest radiographs (CXRs) are commonly performed in emergency units (EUs), but the interpretation requires radiology experience. We developed an artificial intelligence (AI) system (precommercial) that aims to mimic board-certified radiolog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa1e3c9beab800e9e55f98db8ab18724
https://pubmed.ncbi.nlm.nih.gov/34352804
https://pubmed.ncbi.nlm.nih.gov/34352804
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 26:895-908
The adaptation of automatic speech recognition systems to a speaker or an environment is important if we are to achieve high speech recognition performance ubiquitously. Recently, deep neural network (DNN) based acoustic models have been made adaptiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5bda09568f03742a5138fbeb3a1cfb0
https://doi.org/10.1109/taslp.2018.2798821
https://doi.org/10.1109/taslp.2018.2798821
Publikováno v:
Computer Speech & Language. 46:388-400
Speech recognition in adverse real-world environments is highly affected by reverberation and nonstationary background noise. A well-known strategy to reduce such undesired signal components in multi-microphone scenarios is spatial filtering of the m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dccc7b78223aa67ec94fe7fb1233cf0e
https://doi.org/10.1016/j.csl.2017.02.005
https://doi.org/10.1016/j.csl.2017.02.005
Publikováno v:
IWAENC
Polynomial beamforming is an effective approach for flexible beam steering of a data-independent beamformer. To this end, the beamformer is designed for a finite set of predefined Prototype Look Directions (PLDs) and its main beam is steered by polyn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29088446498d7e2d413398d8e34a2fdf
https://doi.org/10.1109/iwaenc.2018.8521287
https://doi.org/10.1109/iwaenc.2018.8521287
Publikováno v:
SAM
In this paper, the elitist resampling particle filter (ERPF) is introduced. The ERPF is an approach to combine generic particle filters, i.e., the SIS and SIR particle filter, based on an evolutionary selection of particles, which introduces a longte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d97ccae342363210b7199db1db7b20dd
https://doi.org/10.1109/sam.2018.8448400
https://doi.org/10.1109/sam.2018.8448400
Publikováno v:
ICASSP
This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4d7a224432257b81fe098eacfd2637e
https://doi.org/10.1109/icassp.2018.8461300
https://doi.org/10.1109/icassp.2018.8461300