no code implementations • 15 Mar 2024 • George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort.
no code implementations • 29 Jan 2024 • Jie Liu, Wenzhe Yin, Haochen Wang, Yunlu Chen, Jan-Jakob Sonke, Efstratios Gavves
Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features.
no code implementations • 20 Jan 2024 • Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set.
1 code implementation • 16 Dec 2023 • Samuele Papa, Riccardo Valperga, David Knigge, Miltiadis Kofinas, Phillip Lippe, Jan-Jakob Sonke, Efstratios Gavves
In this work, we propose $\verb|fit-a-nef|$, a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets, resulting in a significant speed-up.
no code implementations • 27 Nov 2023 • George Yiasemis, Nikita Moriakov, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable.
1 code implementation • 20 Nov 2023 • Joren Brunekreef, Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen
If one only requires only marginal calibration on the image level, this calibration set consists of all individual pixels in the images available for calibration.
no code implementations • 10 Oct 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging.
no code implementations • 18 Sep 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI.
no code implementations • 17 Jul 2023 • Samuele Papa, David M. Knigge, Riccardo Valperga, Nikita Moriakov, Miltos Kofinas, Jan-Jakob Sonke, Efstratios Gavves
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction.
1 code implementation • 9 Feb 2023 • Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions.
1 code implementation • 25 Jan 2023 • David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data.
no code implementations • 20 Jan 2023 • George Yiasemis, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models.
no code implementations • 9 Jan 2023 • Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, Jan-Jakob Sonke, Efstratios Gavves
However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction.
Ranked #40 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
no code implementations • CVPR 2022 • Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, Efstratios Gavves
Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels.
Ranked #32 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
3 code implementations • CVPR 2022 • George Yiasemis, Jan-Jakob Sonke, Clarisa Sánchez, Jonas Teuwen
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
no code implementations • 28 Oct 2021 • Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
no code implementations • 29 Sep 2021 • Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
1 code implementation • 17 Aug 2021 • George Yiasemis, Chaoping Zhang, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
In spite of its extensive adaptation in almost every medical diagnostic and examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow imaging modality which limits its use for dynamic imaging.
1 code implementation • 14 Dec 2020 • Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst, Serge O. Dumoulin, Jan-Jakob Sonke, Frans M. Vos, Matthan W. A. Caan
The IndRNN is an efficient recurrent unit, reducing inference time by 68\% compared to CS, whereas maintaining performance.