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Articles

[2409.03470] Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

Sep 5, 2024 | arxiv.org | de Ridder
Reconstructions of Jupiter’s magnetic field using physics-informed neural networks

Aug 8, 2024 | academic.oup.com | de Ridder

The Juno mission, launched in 2011 (Bolton, 2010), has revolutionized our understanding of Jupiter’s interior through the collection of both gravity and magnetic measurements in orbit since 2016. These new data have not only allowed new constraints on the density structure and zonal flow in the outermost parts of the planet (Kaspi et al. 2018; Militzer et al. 2022), but have permitted new reconstructions of the magnetic field to unprecedented resolution (e.g. Connerney et al. 2017, 2022).
Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images

Jul 14, 2024 | arxiv.org | de Ridder |Van Waeyenberge |Vic De Ridder |Víctor Blanco
A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Nov 9, 2023 | frontiersin.org | Universitair Ziekenhuis Brussel |Vrije Universiteit Brussel |Michael David |de Ridder

1 IntroductionMagnetic resonance-guided radiotherapy (MRgRT) allows plan adaptation on the magnetic resonance imaging (MRI) of the day, offering new perspectives in pelvic cancer treatment. Besides structure tracking and automated beam gating, the MR-linac combination benefits a higher soft tissue contrast and allows on-table plan adaptation (1).
Side-by-side comparison of the two widely studied GRPR radiotracers, radiolabeled NeoB and RM2, in a preclinical setting

Sep 15, 2023 | paperity.org | de Ridder

European Journal of Nuclear Medicine and Molecular Imaging, Aug 2023 NeoB and RM2 are the most investigated gastrin-releasing peptide receptor (GRPR)–targeting radiotracers in preclinical and clinical studies. Therefore, an extensive side-by-side comparison of the two radiotracers is valuable to demonstrate whether one has advantages over the other. Accordingly, this study aims to compare the in vitro and in vivo characteristics of radiolabeled NeoB and RM2 to guide future clinical studies.
SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

Jul 17, 2023 | arxiv.org | de Ridder |Van Waeyenberge

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2306.15194] Chronic pain detection from resting-state raw EEG signals using improved feature selection

Jun 27, 2023 | arxiv.org | de Ridder

arXiv:2306.15194 (cs) Download a PDF of the paper titled Chronic pain detection from resting-state raw EEG signals using improved feature selection, by Jean Li and 4 other authors Download PDF Submission history From: Jeremiah Deng [ view email] [v1] Tue, 27 Jun 2023 04:28:52 UTC (774 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links...
Trends in adults’ energy imbalance gaps over two decades in Belgium using system dynamics modelling - BMC Nutrition

May 27, 2023 | bmcnutr.biomedcentral.com | de Ridder

Positive values for the EIG indicate gaining weight on average, the negative values indicate losing weight, and zero values reflect no change in BMI. In the heat maps shown in Figs. 1 and 2, the green colour shows relatively small values for the estimated EIG, the yellow colour shows intermediate values, and the red colour shows large values. Figure 1 shows the overall estimated EIG for different subpopulations in Belgium.
Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling - Genome Biology

Apr 11, 2023 | genomebiology.biomedcentral.com | de Ridder

ReferencesRang FJ, Kloosterman WP, de Ridder J. From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biol. 2018;19(1):90. https://doi.org/10.1186/s13059-018-1462-9. https://genomebiology-biomedcentral-com.proxy.library.uu.nl/articles/10.1186/s13059-018-1462-9. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021;39(11):1348–65. https://doi.org/10.1038/s41587-021-01108-x.

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de Ridder

Articles

[2409.03470] Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

Reconstructions of Jupiter’s magnetic field using physics-informed neural networks

Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Side-by-side comparison of the two widely studied GRPR radiotracers, radiolabeled NeoB and RM2, in a preclinical setting

SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

[2306.15194] Chronic pain detection from resting-state raw EEG signals using improved feature selection

Trends in adults’ energy imbalance gaps over two decades in Belgium using system dynamics modelling - BMC Nutrition

Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling - Genome Biology

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