To the best of our knowledge, we present the first exploration of combining Swin Transformer and convolution in both the encoder and decoder stages. Through comprehensive comparative analysis, we ...

Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for ...

This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as ...

Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation - convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This ...

This paper proposes a hybrid CNN-transformer network called HCTSpeckle, an encoder-decoder network with a fusion block designed to enhance ultrasound images. The fusion block combines swin ...

This paper presents an advanced offline scheduling scheme to improve the accelerator efficiency, especially for the highly-pruned convolutional neural networks (HP-CNNs). Based on the existing outlier ...

The demand for large-scale data samples has always been a significant bottleneck in the application of artificial intelligence methods in the field of electromagnetic compatibility (EMC) of integrated ...

The proposed encoder–decoder-based ERSCDNet model uses an attention-based encoder and decoder block and a modified new spatial pyramid pooling block at each stage of the decoder part, which ...

This paper proposes a novel two-factor attention based encoder-decoder model (TwoFactorEncoderDecoder) for multivariate weather prediction. The proposed model learns attention weights from two factors ...

In this paper we frame the interpolation problem as a self-learning task using a deep encoder-decoder network. We compare our approach against contemporary interpolation methods on a publicly ...

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction ...