Latest AI and machine learning research in covid-19 for healthcare professionals.
Antibody design remains a critical challenge in therapeutic and diagnostic development, particularly for complex antigens with diverse binding interfaces. Current computational methods face two main limitations: (1) capturing geometric features while preserving symmetries, and (2) generalizing novel antigen interfaces. Despite recent advancements, these methods often fail to accurately capture m...
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-...
Few-shot fine-grained image classification (FS-FGIC) presents a significant challenge, requiring models to distinguish visually similar subclasses w...
Neoadjuvant chemoradiotherapy (NACRT) is the standard treatment for locally advanced rectal cancer (LARC), yet the pathological complete response (pCR...
Large language models (LLMs) are increasingly used in clinical decision support, yet current evaluation methods often fail to distinguish genuine me...
This article investigates matrix-free higher-order discontinuous Galerkin (DG) discretizations of the Navier-Stokes equations for incompressible flo...
Auditory processing difficulties involve challenges in understanding speech in noisy environments despite normal hearing. However, the neural mechan...
Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video g...
Accurate prediction of pedestrian trajectories is essential for applications in robotics and surveillance systems. While existing approaches primari...
Grapevine varieties are essential for the economies of many wine-producing countries, influencing the production of wine, juice, and the consumption...
Recent advances in deep learning have significantly propelled the development of image forgery localization. However, existing models remain highly ...
3D Gaussian Splatting (3DGS) has emerged as a preferred choice alongside Neural Radiance Fields (NeRF) in inverse rendering due to its superior rend...
Staphylococcus aureus (S. aureus) is the leading risk factor for food safety and human health. Herein, a novel wavelength-selective machine learning -...
Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimatel...
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring criti...
In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, oft...
Designing protein-binding proteins with high affinity is critical in biomedical research and biotechnology. Despite recent advancements targeting sp...
T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. Epitope recogniti...
The self-attention mechanism, a cornerstone of Transformer-based state-of-the-art deep learning architectures, is largely heuristic-driven and funda...
Autoregressive generative models naturally generate variable-length sequences, while non-autoregressive models struggle, often imposing rigid, token...