Designed Radiopharmaceuticals: How Machine Learning Is Redefining Precision Cancer Therapy" reports on the integration of deep learning and generative AI in radiopharmaceutical medicine, its impact on ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Deep learning algorithms for ultra-widefield fundus photos can identify retinal detachments with precision, supporting early diagnoses in varied settings. Deep learning (DL) models applied to ...

Abstract: The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have ...

Enhancing enzyme catalytic efficiency and thermal stability is crucial for biocatalytic and industrial applications. While structure-based protein sequence design can improve thermal stability, its ...

An important but unresolved question in deep learning for EEG decoding is which features neural networks learn to solve the task. Prior interpretability studies have mainly explained individual ...

Max Delbrück Center for Molecular Medicine in the Helmholtz Association Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment.

Home Wi-Fi networks are the backbone of how most people get online, connecting laptops, phones, smart TVs and more. When properly secured, they offer a convenient and private way to browse the ...

Abstract: This letter proposes a deep learning-based inverse design framework for two-port electromagnetic(EM) structures, which synergistically integrates deep residual neural networks (ResNet) with ...