In this paper, to overcome scale variation and complex background noise, we propose a novel scale-aware and global contextual network (SGCNet) that employs multi-scale attention mechanisms to selectively strengthen features with different network scales. The key component of SGCNet is a multi-...
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Unfortunately, due to the diversity of tumors and complex boundaries, it is insufficient to obtain contextual data on tumor cells and their surroundings from a single stage. To overcome this limitation, we proposed a Scale-wise Global Contextual Axile Reverse Attention Network (SGC-ARANet) ...
SGCNet: a scale-aware and global context network for linear lesion segmentation in MCSL fundus images of high myopiadoi:10.1117/12.2579889Image segmentationImage fusionEyeFeature extractionLaser vision correctionAt present, high myopia has become a hot spot for eye diseases worldwide because of its ...
SGC-ARANet: scale-wise global contextual axile reverse attention network for automatic brain tumor segmentationGlobal multi-level guidanceScale-wise multi-level blend moduleAxile reverse attentionSegmentationPartial decoderBrain tumorApplied Intelligence - Automated brain tumour segmentation using magnetic ...
In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and ...