This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced...
and we propose EPSPPM to enhance the feature receptive field. Secondly, the FAFM is employed for adaptive fusion of dual-modality feature information. Then, the DAPM is designed to use upper and lower pyramid paths for multiscale fusion. Finally, the result is obtained through the detection hea...
we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention ...
The heatmap visualization highlights the intensity distribution in a 2D heat map by displaying a slice of the Flair modality. This aids in comprehending how pixel intensities vary spatially. The Flair modality's structure outlines are seen on the contour plot. It gives the tumor boundaries a visu...
To this end, we proposed the Correlation-Driven feature Decomposition Fusion (CDDFuse) model, where modality- specific and modality-shared feature extractions are realized by a dual-branch encoder, with the fused image reconstructed by the decoder...
In recent years, significant progress has been made in MMIF tasks due to advances in deep neural networks. However, existing methods cannot effectively and efficiently extract modality-specific and modality-fused features constrained by the inherent local reductive bias (CNN) or quadratic computational ...
Within the feature-level, we are able to explore the inter- action between raw features across modalities, but it also need avoiding to potentially suppress the modality-specific interaction. Furthermore, the raw features represent different physical properties of the signals in the respective modal-...
Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion. IEEE TCSVT, 2021. [13] Jinyuan Liu, Yuhui Wu, Zhanbo Huang, Risheng Liu, and Xin Fan. Smoa: Searching a modality-oriented architecture for infrared and...
In order to capture more rich feature semantic information, we incorporate multi-head attention mechanism into the model. Multi-modal features in video modality are fused by feature fusion module to increase semantic consistency among different modalities. In order to enhance the alignment between ...
“Mid-term fusion”: the model performs alternating fusion of the two high-level feature maps corresponding to the two ultrasound image modalities during the mid-term, with equal contribution from each modality. “Adaptive fusion”: the model performs alternating fusion of the two high-level featur...