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Found 6 papers, 2 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.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
Generalization of the Change of Variables Formula with Applications to Residual Flows
Normalizing flows leverage the Change of Variables Formula (CVF) to define flexible density models.
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.
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.
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models.
