179 citations · 292 across the 5 of their papers we have counts for
5 papers
Concept Based Explanations and Class Contrasting
Rudolf Herdt, Daniel Otero Baguer
Explaining deep neural networks is challenging, due to their large size and non-linearity. In this paper, we introduce a concept-based explanation method, in order to explain the p…
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer +2
In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing…
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)
Jean Le'Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer +6
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to a…
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
Daniel Otero Baguer, Johannes Leuschner, Maximilian Schmidt
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the exist…
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer +1
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field. Comparing these approaches is…