STM-DLP is proposed, a simple yet effective Spatial-Temporal Multitask Deep Learning Pipeline trained on the calculation re-sults of regular images that overcomes those irregular images frustrating calculation approach by predicting parameters with sound values, accommodating non-uniform framing intervals, and achieving robustness against image artifacts and reduced frames.
The potential of DeepCTE3D in enhancing clinical triage and advancing neuroimaging applications, especially in scenarios where MRI is not feasible is highlighted, especially in scenarios where MRI is not feasible.
B. Pinto, Tayran Milá Mendes Olegário, Pedro Vinicius Alves Silva et al.· Scientific Reports· 0 citations
A deep learning-based super-resolution framework that integrates multi-scale feature extraction and attention mechanisms to enhance the quality of 4D flow MRI data and support the use of deep learning to extend the clinical utility of 4D flow MRI for non-invasive hemodynamic assessment.
This study introduces a novel 3D registration framework centered on a dynamic wavelet transform module that achieves superior registration fidelity, highlighting its potential for practical clinical implementation.
Bo-Hua Chu, Bao-Ju Zhang, Bo Zhang et al.· Interdisciplinary Sciences C...· 0 citations
MR-JEPA is presented, a self-supervised video foundation model for CMR that extends LeJEPA to 3D spatiotemporal inputs through tubelet tokenization, spatiotemporal masking augmentation, and initialization from a 2D CMR foundation model.
Athira J. Jacob, Puneet Sharma, D. Comaniciu et al.· 0 citations
Accurate classification of retinal diseases from optical coherence tomography (OCT) images is important for clinical decision support. However, many studies in deep learning put the primary focus on enhancing accuracy and often provide limited attention to calibration and robustness under real-world variations. This wo...
Mithun Vijayan, Goutham Veerapu, N. S.· Frontiers in Artificial Inte...· 0 citations
The Frequency Attention Residual Denoising (FARD) network is proposed, a multi-contrast fusion model that leverages both spatial and frequency-domain processing to enhance apparent isotropic detail on a 1mm3 grid for all contrasts from these complementary inputs.
Alexander Nazarov, N. Kiryati, Dani Roizen et al.· Medical Image Analysis· 0 citations
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