HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
HybridCTrm: Bridging CNN and transformer for multimodal brain image segmentation
Multimodal medical image segmentation is always a critical problem in medical image
segmentation. Traditional deep learning methods utilize fully CNNs for encoding given …
segmentation. Traditional deep learning methods utilize fully CNNs for encoding given …
3-D fully convolutional networks for multimodal isointense infant brain image segmentation
Accurate segmentation of infant brain images into different regions of interest is one of the
most important fundamental steps in studying early brain development. In the isointense …
most important fundamental steps in studying early brain development. In the isointense …
Unpaired multi-modal segmentation via knowledge distillation
Multi-modal learning is typically performed with network architectures containing modality-
specific layers and shared layers, utilizing co-registered images of different modalities. We …
specific layers and shared layers, utilizing co-registered images of different modalities. We …
TranSiam: Aggregating multi-modal visual features with locality for medical image segmentation
Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …
diseases. On single-modal data, convolutional neural networks have demonstrated …
Multimodal medical image segmentation using multi-scale context-aware network
X Wang, Z Li, Y Huang, Y Jiao - Neurocomputing, 2022 - Elsevier
Multimodal medical image segmentation with different imaging devices is a key but
challenging task in medical image visual analysis and reasoning. Recently, U-Net based …
challenging task in medical image visual analysis and reasoning. Recently, U-Net based …
Flexible fusion network for multi-modal brain tumor segmentation
Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive
volumetric studies and quantitative analysis of early brain development. However …
volumetric studies and quantitative analysis of early brain development. However …
MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation
Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …
Self-supervised multi-modal hybrid fusion network for brain tumor segmentation
Accurate medical image segmentation of brain tumors is necessary for the diagnosing,
monitoring, and treating disease. In recent years, with the gradual emergence of multi …
monitoring, and treating disease. In recent years, with the gradual emergence of multi …