Latest AI and machine learning research in covid-19 for healthcare professionals.
Vision-based deep reinforcement learning involves dealing with high-dimensional inputs of image information. It is crucial to abstract effective states from high-dimensional image inputs and limited samples for sample-efficient reinforcement learning. To address this challenge, inspired by fields such as natural language processing and computer vision, we propose a self-supervised task based on ma...
Genome editing enzymes can introduce targeted changes to the DNA in living cells, transforming biological research and enabling the first approved gene editing therapy for sickle cell disease. However, their genome-wide activity can be altered by genetic variation at on- or off-target sites, potentially impacting both their precision and therapeutic safety. Due to a lack of scalable methods to mea...
Reliable instance-level scene understanding is a fundamental prerequisite for object-level interactions and high-fidelity 3D representations. While cu...
Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead do...
Advancements in immunogenomics and immuno-oncology have enabled the development of personalized cancer vaccines (PCVs) that target cancer cell-specifi...
Background Endometriosis is a complex, estrogen-dependent disease with a strong genetic component. Although genome-wide association studies (GWAS) hav...
Generating high-quality novel views at real-time frame rates remains a central challenge in 3D vision, particularly in sparse-view scenarios. Neural r...
Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematica...
This paper presents an automatic system for recognizing pulmonary diseases in chest X-rays using geometric normalization of the lung region. The metho...
Vision-based aerial tracking is critical in GPS-denied environments. Reliable perception for tracking depends on large-scale labeled data, yet most ph...
Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redund...
Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks...
High-resolution Diffusion Transformer (DiT) inference contains substantial spatial redundancy, but many spatially adaptive implementations encode regi...
Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recent...
Background: Single-cell foundation models are increasingly used for perturbation prediction and gene network inference, but their learned gene represe...
From protein structure prediction to novel protein generation, challenging protein engineering tasks have been made possible by advancements in machin...
Background: Sequence-to-function (S2F) deep learning models are increasingly used to prioritize non-coding regulatory variants, but their behavior acr...
Prediction of B-cell epitopes can assist in reducing costly wet-lab screening in vaccine design, diagnostics, and antibody discovery. However, current...
Image-based virtual try-on has emerged as a compelling task in e-commerce and augmented reality, yet existing methods struggle to simultaneously prese...
Constructing simulation-ready 3D scenes from multi-view captures is a key bottleneck for Embodied Artificial Intelligence, as downstream tasks require...