Biomedical physics & engineering express
Jan 5, 2026
Accurate detection and segmentation of polyps during colonoscopy are of great significance for the early prevention and treatment of colorectal cancer. However, due to the considerable variations in polyp size and shape, as well as their blurred boun...
In-air signature is a behavioral biometric trait that has gained increasing attention in recent years due to its contactless nature and potential for secure, hygienic, and remote authentication. Unlike traditional pen-and-paper or tablet-based system...
During radiotherapy, the radiation dose delivered to circulating blood can result in radiation-induced lymphopenia, which is correlated with adverse clinical outcomes like lower survival. Increasingly complex models to simulate radiation dose deliver...
Pigmentation varies widely across humans and is shaped by melanin quantity, type, and spatial distribution. Retinal pigmentation protects against light-induced damage, yet its genetic and evolutionary bases remain unclear. We developed a deep learnin...
Journal of chemical information and modeling
Dec 31, 2025
The accurate and efficient computational identification of interferon-gamma-inducing epitopes (IFNgIE) is a critical bottleneck in the design of next-generation vaccines and immunotherapies. Existing computational models, while adept at learning sequ...
Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning meth...
INTRODUCTION: Visual Patient Predictive (VPP) is an AI-based extension of the Visual Patient Avatar (VPA) that integrates deep learning models to predict upcoming vital sign deviations and display them as dashed visual elements. By explicitly showing...
Deep learning for continuous physiological signals, such as electrocardiography or oximetry, has achieved remarkable success in supervised learning scenarios where training and testing data are drawn from the same distribution. However, when evaluati...
Traditional asset allocation rules, while effective in stable phases, tend to erode once markets enter volatile regimes or undergo structural breaks. Research in deep reinforcement learning (DRL) has usually emphasized raw-return rewards, leaving asi...
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