G
Georg Wimmer
Researcher at University of Salzburg
Publications - 46
Citations - 619
Georg Wimmer is an academic researcher from University of Salzburg. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 11, co-authored 36 publications receiving 447 citations.
Papers
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Journal ArticleDOI
Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification
TL;DR: This work explores Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities and suggests the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.
Journal ArticleDOI
Directional wavelet based features for colonic polyp classification
Georg Wimmer,Toru Tamaki,Jens J. W. Tischendorf,Michael Häfner,Shigeto Yoshida,Shinji Tanaka,Andreas Uhl +6 more
TL;DR: It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transforms and the Shearlet transform.
Proceedings ArticleDOI
CNN transfer learning for the automated diagnosis of celiac disease
TL;DR: It is shown that fine-tuning all the layers of the nets achieves the best results and outperforms the comparison approaches.
Journal ArticleDOI
Local fractal dimension based approaches for colonic polyp classification.
TL;DR: In this article, texture analysis methods that are based on computing the local fractal dimension (LFD; or also called the local density function) and applies them for colonic polyp classification are introduced.
Journal ArticleDOI
Scale invariant texture descriptors for classifying celiac disease
TL;DR: This work test several approaches for the computer assisted diagnosis of celiac disease and some of the methods improve the state of the art in detecting Celiac disease.