73 citations · 146 across the 16 of their papers we have counts for
18 papers
Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior
Chinmay Rao, Efe Ilıcak, Matthias J. P. van Osch +5
Multi-contrast MR scans contain redundant structural information that can be leveraged during reconstruction and potentially accelerate acquisition times. This idea has inspired en…
KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction
Donghang Lyu, Marius Staring, Mariya Doneva +2
Cardiac Magnetic Resonance (CMR) imaging is a non-invasive method for assessing cardiac structure, function, and blood flow. Cine MRI extends this by capturing heart motion, provid…
NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography
Kirsten W. H. Maas, Danny Ruijters, Nicola Pezzotti +1
Three-dimensional (3D) and dynamic 3D+time (4D) reconstruction of coronary arteries from X-ray coronary angiography (CA) has the potential to improve clinical procedures. However,…
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
Donghang Lyu, Chinmay Rao, Marius Staring +4
Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have b…
Progressive Monitoring of Generative Model Training Evolution
Vidya Prasad, Anna Vilanova, Nicola Pezzotti
While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue. With their g…
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Chinmay Rao, Matthias van Osch, Nicola Pezzotti +8
Since the various MR contrasts of a given anatomy contain redundant information, one contrast can be used to guide the reconstruction of another undersampled contrast acquired subs…
NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
Kirsten W. H. Maas, Danny Ruijters, Anna Vilanova +1
Dynamic three-dimensional (4D) reconstruction from two-dimensional X-ray coronary angiography (CA) remains a significant clinical problem. Existing CA reconstruction methods often…
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models
Vidya Prasad, Hans van Gorp, Christina Humer +3
Diffusion models, widely used in image generation, rely on iterative refinement to generate images from noise. Understanding this data evolution is important for model development…
Unraveling the Temporal Dynamics of the Unet in Diffusion Models
Vidya Prasad, Chen Zhu-Tian, Anna Vilanova +3
Diffusion models have garnered significant attention since they can effectively learn complex multivariate Gaussian distributions, resulting in diverse, high-quality outcomes. They…
Class-constrained t-SNE: Combining Data Features and Class Probabilities
Linhao Meng, Stef van den Elzen, Nicola Pezzotti +1
Data features and class probabilities are two main perspectives when, e.g., evaluating model results and identifying problematic items. Class probabilities represent the likelihood…
Image Quality Assessment for Magnetic Resonance Imaging
Segrey Kastryulin, Jamil Zakirov, Nicola Pezzotti +1
Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models…
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl
Nicola Pezzotti
This paper describe an hybrid agent trained to play in Fantasy Football AI which participated in the Bot Bowl III competition. The agent, MimicBot, is implemented using a specifica…
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy +9
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence…
Learning to Predict Error for MRI Reconstruction
Shi Hu, Nicola Pezzotti, Max Welling
In healthcare applications, predictive uncertainty has been used to assess predictive accuracy. In this paper, we demonstrate that predictive uncertainty estimated by the current m…
Adaptive-CS-Net: FastMRI with Adaptive Intelligence
Nicola Pezzotti, Elwin de Weerdt, Sahar Yousefi +9
Adaptive intelligence aims at empowering machine learning techniques with the extensive use of domain knowledge. In this work, we present the application of adaptive intelligence t…
Progressive Data Science: Potential and Challenges
Cagatay Turkay, Nicola Pezzotti, Carsten Binnig +7
Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iter…
GPGPU Linear Complexity t-SNE Optimization
Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev +5
The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high…
Approximated and User Steerable tSNE for Progressive Visual Analytics
Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten +3
Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One ke…