Residual Channel Attention Network for Brain Glioma Segmentation
A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing algorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate features for glioma segmentation. The proposed channel attention mechanism adaptively weights feature channel-wise to optimize the latent representation of gliomas. We evaluate our method on the established dataset BraTS2017. Experimental results indicate the superiority of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSimilar Papers 제목 키워드 기반
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation
Gliomas are among the most common malignant brain tumors and are characterized by considerable heterogeneity, which complicates accurate detection and segmentation. Multi-modal MRI is the clinical standard for glioma ima…
Brain Tumor SegmentationSegmentationTumor SegmentationAdaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)
Glioma is a harmful brain tumor that requires early detection to ensure better health results. Early detection of this tumor is key for effective treatment and requires an automated segmentation process. However, it is a…
Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an …
Computational EfficiencyBrain Tumor SegmentationSemantic SegmentationCross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images
According to the 2021 World Health Organization (WHO) Classification scheme for gliomas, glioma segmentation is a very important basis for diagnosis and genotype prediction. In general, 3D multimodal brain MRI is an effe…
DiagnosticBrain Tumor Segmentation from MRI Images using Deep Learning Techniques
A brain tumor, whether benign or malignant, can potentially be life threatening and requires painstaking efforts in order to identify the type, origin and location, let alone cure one. Manual segmentation by medical spec…
Brain Tumor ClassificationBrain Tumor SegmentationDeep LearningImage Segmentation+4