At the core of their effectiveness is a sophisticated combination of deep learning techniques and innovative architecture ... to each token. Decoder: Uses the encoder’s outputs, along with ...

Abstract: Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient ...

Attention mechanisms, especially in transformer models, have significantly enhanced the performance of encoder-decoder architectures, making them highly effective for a wide range of ...

The concept of Deep Learning (DL) that was purely theoretical less ... to a corresponding output token due to the lack of a 1:1 mapping. The RNN encoder–decoder neural network architecture, introduced ...

A few deep learning approaches have been developed to improve ... Figure 3 illustrates the LEQNet architecture. Each encoder and decoder of LEQNet consists of five layers, and the deeper bottleneck ...

To this end, we introduce, a multi-scale encoder-decoder self-attention (MEDUSA) mechanism tailored for medical image analysis. While self-attention deep convolutional ... to train the proposed ...