Search Results for author:
Found 18 papers, 8 papers with code
A study on the adequacy of common IQA measures for medical images
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images.
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization
This study contributes to PFL by establishing a solid theoretical foundation for the proposed method and offering a robust, ready-to-use framework that effectively addresses the challenges posed by non-IID data in FL.
A study of why we need to reassess full reference image quality assessment with medical images
Image quality assessment (IQA) is not just indispensable in clinical practice to ensure high standards, but also in the development stage of novel algorithms that operate on medical images with reference data.
The curious case of the test set AUROC
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace.
Recent Methodological Advances in Federated Learning for Healthcare
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
Reinterpreting survival analysis in the universal approximator age
We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning.
Navigating the challenges in creating complex data systems: a development philosophy
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
SELTO: Sample-Efficient Learned Topology Optimization
Recent developments in Deep Learning (DL) suggest a vast potential for Topology Optimization (TO).
Classification of datasets with imputed missing values: does imputation quality matter?
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
Unsupervised Learning of the Total Variation Flow
Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, an unsupervised neural network approach, to approximate the solution of the TV flow given an initial image and a time instance.
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset
Magnetic particle imaging (MPI) is an imaging modality exploiting the nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-dependent concentration of a tracer consisting of these nanoparticles.
Ground Truth Free Denoising by Optimal Transport
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.
A Projectional Ansatz to Reconstruction
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors.
Invariance and Inverse Stability under ReLU
We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping.
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.
Singular Values for ReLU Layers
By presenting on the one hand theoretical justifications, results, and interpretations of these two concepts and on the other hand numerical experiments and results of the ReLU singular values and the Gaussian mean width being applied to trained neural networks, we hope to give a comprehensive, singular-value-centric view of ReLU layers.
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.
