Search Results for author:
Found 6 papers, 3 papers with code
Smooth Deep Saliency
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.
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
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.
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field.
Regularization by architecture: A deep prior approach for inverse problems
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems.
