Deep learning based multimodal biomedical data fusion: An overview and comparative review
J Duan, J Xiong, Y Li, W Ding - Information Fusion, 2024 - Elsevier
Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …
actionable insights by seamlessly integrating disparate biomedical data from multiple …
[HTML][HTML] Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review
Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-
invasive and highly detailed look into the human body. However, the long acquisition times …
invasive and highly detailed look into the human body. However, the long acquisition times …
Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images
Latent Low-Rank Representation (LatLRR) has emerged as a prominent approach for fusing
visible and infrared images. In this approach, images are decomposed into three …
visible and infrared images. In this approach, images are decomposed into three …
Ensemble-based multimodal medical imaging fusion for tumor segmentation
A Karthik, HSA Hamatta, S Patthi, C Krubakaran… - … Signal Processing and …, 2024 - Elsevier
The use of multimodal medical imaging is on the rise, both in academic and clinical settings.
There was a meteoric growth in the use of multimodal imaging analysis (MIA) with the …
There was a meteoric growth in the use of multimodal imaging analysis (MIA) with the …
Information maximized U-Nets for vestibular schwannoma segmentation using MRI with missing modality
Several Magnetic Resonance Imaging (MRI) sequences are acquired for diagnosis and
treatment. MRI with excellent soft-tissue contrast is desired for post-processing algorithms …
treatment. MRI with excellent soft-tissue contrast is desired for post-processing algorithms …