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Found 6 papers, 5 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.
CAFCT: Contextual and Attentional Feature Fusions of Convolutional Neural Networks and Transformer for Liver Tumor Segmentation
Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans.
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.
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Blood cell detection is a typical small-scale object detection problem in computer vision.
