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Found 6 papers, 4 papers with code
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities
Our ablation study demonstrates the importance of the proposed modules with CNN-Transformer networks and the convolutional blocks in Transformer for improving the performance of brain tumor segmentation with missing modalities.
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization
The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component.
ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation
We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation.
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection.
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection.
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC.
