Specifically, we introduce a double decoder adversarial autoencoder (DDAAE) to align MRIs from different protocols. The aligned MRI images are then integrated into our proposed ensemble residual soft shrinkage threshold attention (ERS 2 TA) diagnostic network for disease diagnosis. This framework not ...
Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative mod
The results show that the ablation of either the multi-task learning or the discriminator (termed dual-autoencoder) reduced the average prediction accuracy of the network (Fig. 2c,“Methods”). Together, these benchmark studies and ablation analysis demonstrate the effectiveness of implementing the...
To overcome this limitation, we introduce the dual-masked autoencoders (dual-MAE) algorithm, consisting of online and target networks with encoder and decoder modules. These networks are optimized by minimizing three losses: between the reconstructed image of the online network and the target ...
machine-learningcnnpytorchattention-mechanismimagingmultimodalitymultivariate-analysisvariational-autoencoderdata-fusionmultimodalmultimodal-deep-learningmulti-view-learningmulti-viewgraph-neural-networkpytorch-lightning UpdatedFeb 7, 2025 Python AstraZeneca/SubTab ...
2021TIPMulti-Interactive Dual-Decoder for RGB-Thermal Salient Object Detection Zhengzheng Tu, et al.Paper/Code 2021TCYBASIF-Net: Attention Steered Interweave Fusion Network for RGB-D Salient Object Detection Chongyi Li, Runmin Cong, et al.Paper/Code ...
[2023.04] OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction [paper] [github] [2023.03] SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [paper] [github] [2023.03] OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Percepti...
[55], they use the input-level fusion strategy to directly integrate the different modalities in the input space, they apply the encoder-decoder structure of CNN combined with an additional VAE (variational autoencoder) branch to the encoder part. The VAE branch can reconstruct the input image ...
A novel multi-layered steganographic framework is proposed, integrating Huffman coding, Least Significant Bit (LSB) embedding, and a deep learning-based encoder–decoder to enhance imperceptibility, robustness, and security. Huffman coding compresses data and obfuscates statistical patterns, enabling ...
doubling the number of convolution layer filters after each downsampling. The encoder is connected to the decoder through a series of two 3×3 convolutional operations. On the other hand, the decoder first up-samples the feature map using a 2×2 transposed convolutional operation, resulting in ...