Zobrazeno 1 - 10
of 609
pro vyhledávání: '"Egan, Gary"'
Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction pro
Externí odkaz:
http://arxiv.org/abs/2409.06198
Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for large training datasets. Although INR methods have been studied for medi
Externí odkaz:
http://arxiv.org/abs/2409.01013
Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in approximately 30% of clinical MRI scans; however, motion has not been ex
Externí odkaz:
http://arxiv.org/abs/2405.17756
In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. However, image quality is a crucial factor that influences the accuracy of c
Externí odkaz:
http://arxiv.org/abs/2306.00530
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, undersampling the measurements in k-space as well as the over- or under-p
Externí odkaz:
http://arxiv.org/abs/2303.00111
As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two window
Externí odkaz:
http://arxiv.org/abs/2209.07704
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI) scans as a result of close-range proton density and T1/T2 parameters.
Externí odkaz:
http://arxiv.org/abs/2207.08412
Publikováno v:
In Biomedical Signal Processing and Control February 2025 100 Part A
This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adversarial learning approaches. To enhance the generalization across the
Externí odkaz:
http://arxiv.org/abs/2201.03777
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed
Externí odkaz:
http://arxiv.org/abs/2111.13300