no code implementations • 2 Apr 2024 • Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples.
no code implementations • 4 Feb 2023 • Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Jean Le'Clerc Arrastia, Peter Maass
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 a 1x1 convolution.
no code implementations • 5 Mar 2021 • Jean Le'Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, Peter Maaß
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine.
1 code implementation • 10 Mar 2020 • 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.
1 code implementation • 1 Oct 2019 • Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field.
2 code implementations • 10 Dec 2018 • Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems.