Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
SuperFusion: A versatile image registration and fusion network with semantic awareness
Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …
fused image comprehensively characterizing the imaging scene. However, existing image …
Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation
Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …
treatment. Accurate segmentation results are critical for localization and spatial distribution …
Infrared and visible image fusion via multiscale receptive field amplification fusion network
C Ji, W Zhou, J Lei, L Ye - IEEE Signal Processing Letters, 2023 - ieeexplore.ieee.org
Infrared and visible image fusion, which highlights radiometric and detailed texture
information and completely and accurately describes objects, is a long-standing and well …
information and completely and accurately describes objects, is a long-standing and well …
Local extreme map guided multi-modal brain image fusion
Multi-modal brain image fusion targets on integrating the salient and complementary
features of different modalities of brain images into a comprehensive image. The well-fused …
features of different modalities of brain images into a comprehensive image. The well-fused …
[HTML][HTML] Multi-modal tumor segmentation methods based on deep learning: a narrative review
Methods In in the PubMed and Google Scholar databases, the keywords “multi-
modal”,“deep learning”, and “tumor segmentation” were used to systematically search …
modal”,“deep learning”, and “tumor segmentation” were used to systematically search …
Mirror u-net: Marrying multimodal fission with multi-task learning for semantic segmentation in medical imaging
Abstract Positron Emission Tomography (PET) and Computed Tomography (CT) are
routinely used together to detect tumors. PET/CT segmentation models can automate tumor …
routinely used together to detect tumors. PET/CT segmentation models can automate tumor …
Hybridvps: Hybrid-supervised video polyp segmentation under low-cost labels
Deep polyp segmentation methods have shown remarkable potential in boosting diagnostic
efficiency. Nevertheless, these methods rely on sufficient pixel-wise annotated data, which is …
efficiency. Nevertheless, these methods rely on sufficient pixel-wise annotated data, which is …
Breaking free from fusion rule: A fully semantic-driven infrared and visible image fusion
Infrared and visible image fusion plays a vital role in the field of computer vision. Previous
approaches make efforts to design various fusion rules in the loss functions. However, these …
approaches make efforts to design various fusion rules in the loss functions. However, these …
Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images
The state-of-the-art works for the segmentation of brain tumor using the images acquired by
Magnetic Resonance Imaging (MRI) with their performances are analyzed in this …
Magnetic Resonance Imaging (MRI) with their performances are analyzed in this …