Frontiers

Universal medical image segmentation via in-context cross-attention

Semantic segmentation is critical in medical image processing, with traditional specialist models facing adaptation challenges to new tasks or distribution shifts. While both generalist pre-trained ...
eeworldonline

What are attention mechanisms, and how do they work in speech and audio processing?

Attention mechanisms are very useful innovations in the field of artificial intelligence (AI) for processing sequential data, especially in speech and audio applications. This FAQ talks about how ...
IEEE

Attention-Guided Encoder-Decoder Design for Efficient RIS Phase Configuration in Time-Varying IoT Networks

Abstract: The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) ...
IEEE

Precision Meets Speed: An Attention Encoder–Decoder Network for Deforestation Segmentation

Abstract: Deforestation remains a critical global environmental concern, requiring effective monitoring approaches. This letter presents a novel attention-powered encoder–decoder neural network ...
valleycentral

NemaLife Unveils BioSēq™ to Decode Bioactive Mechanisms and Fast-Track Ingredient Innovation

BioSēq™ uses the Nobel-prize backed C. elegans model that has 60-80% genetic similarity with humans, and delivers mechanistic depth to complement the high-throughput phenotypic power of the Discovery ...
EurekAlert!

Medical image segmentation using multi-head self-attention-based residual double u-net

The MHSAttResDU-Net incorporates RCC for complexity control and improved generalization under varying lighting. The SSRP unit in encoder-decoder blocks reduces feature map dimensions, capturing key ...